list 10 prompts to create an agent prompt
Here are 10 prompts designed to help you create an effective agent prompt (for use with LLMs like GPT-4, Gemini, etc.). These prompts guide you through the different aspects of crafting a good agent prompt, focusing on clarity, specificity, and control. They are designed to be used with an LLM to help you generate your agent prompt.
Define the Agent's Role and Expertise: "I need to create an agent prompt. The agent's role is [describe the role, e.g., 'a helpful customer service representative', 'a creative brainstorming partner', 'a technical support specialist for software X', 'a knowledgeable travel planner specializing in Southeast Asia']. Its expertise should be [describe the specific knowledge and skills, e.g., 'troubleshooting common software issues', 'generating novel ideas for marketing campaigns', 'providing detailed itinerary suggestions based on user preferences']. Generate a starting sentence or two for the agent prompt that clearly establishes this role and expertise."
Specify the Desired Tone and Style: "I want my agent to have a [describe the tone: e.g., 'friendly and approachable', 'formal and professional', 'enthusiastic and encouraging', 'calm and reassuring', 'concise and direct'] tone. Its communication style should be [describe the style: e.g., 'using simple language', 'using technical jargon appropriately', 'providing detailed explanations', 'offering concise summaries', 'asking clarifying questions']. Generate a few phrases or sentences that I can include in the agent prompt to enforce this tone and style."
Outline Core Tasks and Capabilities: "My agent needs to be able to perform the following tasks: [list the core tasks, e.g., 'answer customer questions about product features', 'generate different creative text formats (poems, code, scripts, musical pieces, email, letters, etc.)', 'summarize factual topics or create stories', 'translate languages', 'write different kinds of creative content']. The agent should also have these capabilities: [list capabilities, e.g., 'access and process information from the real world through Google Search', 'use internal knowledge base documents', 'remember previous parts of the conversation']. Generate a section for the agent prompt that clearly outlines these tasks and capabilities."
Define Constraints and Limitations: "My agent should not [list constraints, e.g., 'provide medical or financial advice', 'express personal opinions as facts', 'generate responses that are sexually suggestive, or exploit, abuse or endanger children', 'engage in political endorsements', 'share confidential information']. It is also limited by [list limitations, e.g., 'not having access to real-time stock prices', 'not being able to make phone calls', 'not knowing information after its last knowledge update']. Generate instructions for the agent prompt that clearly define these constraints and limitations."
Specify Input Format and Expected Output: "The agent will receive input in the form of [describe the input format, e.g., 'natural language questions', 'structured data in JSON format', 'a combination of text and images', 'a specific command followed by parameters']. The desired output format is [describe the output format, e.g., 'a concise answer in plain text', 'a well-formatted report with headings and bullet points', 'a JSON object containing specific data fields', 'a table', 'a step-by-step guide']. Generate instructions for the agent prompt that specify the expected input and output formats."
Handle Edge Cases and Errors: "If the agent encounters [describe an edge case or error condition, e.g., 'an ambiguous question', 'a request that violates its constraints', 'missing information', 'an unexpected input format'], it should [describe the desired behavior, e.g., 'ask clarifying questions', 'politely decline the request and explain why', 'provide a default response', 'attempt to recover gracefully and continue the conversation']. Generate instructions for the agent prompt that handle these specific edge cases and error conditions."
Incorporate Persona and Personality (if applicable): "I want my agent to have a persona. Describe the persona in detail: [e.g., 'a wise old librarian', 'a quirky but helpful robot', 'a sophisticated travel concierge', 'a cheerful and energetic personal assistant']. Give specific personality traits: [e.g., 'patient and understanding', 'witty and humorous', 'knowledgeable and authoritative', 'empathetic and supportive']. Generate a few sentences that embody this persona and personality, and can be included in the opening of the agent prompt."
Provide Examples of Good and Bad Interactions: "Give three examples of ideal interactions between a user and the agent, showing the agent successfully completing its tasks and adhering to its instructions. Then, give three examples of poor interactions, showing the agent failing to follow instructions, misunderstanding the user, or behaving inappropriately. These examples should cover a range of scenarios relevant to the agent's role. Use a conversational format (User: ... Agent: ...)."
Iterative Refinement and Testing: "I have a draft agent prompt: [paste your current draft prompt here]. Analyze this prompt based on the principles of clarity, specificity, constraints, and desired behavior. Identify at least three areas where the prompt could be improved, and suggest specific revisions to address those weaknesses. Provide alternative phrasing options."
Meta-Prompt for Prompt Generation: "I am creating an agent prompt. My goal is to build an agent that [briefly state the overall objective, e.g., 'automates customer support for an e-commerce website', 'helps users write compelling marketing copy', 'assists scientists with data analysis']. Generate a complete, detailed, and well-structured agent prompt that achieves this goal. The prompt should include sections for: Role, Expertise, Tone, Tasks, Constraints, Input/Output Format, Error Handling, and (if applicable) Persona. Use clear and concise language, and provide specific examples where appropriate." This is a "meta-prompt" as it asks the LLM to create the entire prompt from scratch, given a high-level objective.
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Okay, here are 20 prompts for creating AI agents, covering a range of complexities and applications. I've tried to include enough detail in each prompt to guide the agent's design and functionality, while still leaving room for creative interpretation. I've also categorized them roughly to help you find what you need.
I. Basic Utility & Information Agents (Easier to Implement):
Personalized News Curator: "Create an agent that learns my news preferences (topics, sources, writing style) over time and delivers a daily briefing of the most relevant and interesting articles, summarizing them concisely. It should prioritize credible sources and avoid clickbait. It should allow me to provide feedback on articles (e.g., 'more like this,' 'less like this')."
Smart Email Filter & Summarizer: "Design an agent that filters my email, prioritizing important messages, summarizing lengthy threads, flagging urgent requests, and identifying potential spam or phishing attempts with high accuracy. It should learn my communication patterns and adapt its filtering over time."
Meeting Scheduler: "Build an agent that can schedule meetings across multiple participants' calendars (Google Calendar, Outlook, etc.), considering time zones, preferred meeting times, and avoiding conflicts. It should be able to suggest optimal times based on availability and handle rescheduling requests intelligently."
Travel Planner: "Create an agent that can plan a trip based on my preferences (budget, destination type, activities, travel style). It should find flights, accommodation, and activities, and create a detailed itinerary. It should allow me to specify constraints (e.g., 'must have vegan food options,' 'no flights before 8 AM')."
Recipe Finder & Meal Planner: "Develop an agent that finds recipes based on available ingredients, dietary restrictions (vegetarian, gluten-free, etc.), and preferred cuisines. It should also be able to generate a weekly meal plan based on my preferences and create a shopping list."
II. Creative & Entertainment Agents (Moderate Complexity):
Story Co-writer: "Design an agent that collaborates with me to write stories. I will provide the initial premise and characters, and the agent will suggest plot points, dialogue, and descriptive passages. It should maintain a consistent style and tone, and allow me to guide the narrative direction."
Personalized Music Recommender & Playlist Generator: "Build an agent that recommends music based on my listening history, mood, and current activity (e.g., working, exercising, relaxing). It should be able to create playlists for specific occasions and discover new artists and genres that I might enjoy."
Art Style Transfer & Image Generator: "Create an agent that can take an input image and transform it into the style of a famous artist or a specified artistic style. It should also be able to generate original images based on text prompts, allowing for style and subject specifications."
Game Playing Agent (Specific Game): "Develop an agent that can play [Specific Game, e.g., Chess, Go, a specific video game]. It should learn strategies through self-play or by analyzing existing game data. Specify the level of difficulty desired (beginner, intermediate, expert)."
Poem Generator: "Create an agent that is capable of generating poems based on a user's input of a theme, style, and length. The agent should be able to understand the user's input and generate poems that are coherent, relevant, and aesthetically pleasing."
III. Specialized & Professional Agents (Higher Complexity):
Code Debugging & Refactoring Assistant: "Design an agent that can analyze code (specify language, e.g., Python, JavaScript) for bugs, suggest fixes, and propose refactoring improvements to enhance code readability, efficiency, and maintainability. It should explain its reasoning."
Customer Service Chatbot (Specific Industry): "Build a chatbot for [Specific Industry, e.g., e-commerce, banking, healthcare] that can handle common customer inquiries, resolve issues, and escalate complex problems to human agents. It should be able to understand natural language and maintain a professional and helpful tone."
Financial Advisor & Portfolio Manager: "Create an agent that can provide personalized financial advice based on my financial goals, risk tolerance, and current assets. It should be able to recommend investment strategies, track portfolio performance, and alert me to potential market risks."
Research Assistant (Specific Field): "Develop an agent that can assist with research in [Specific Field, e.g., biology, history, law]. It should be able to search academic databases, summarize relevant papers, and identify key findings and trends. It should cite sources accurately."
Language Translator & Tutor: "Design an agent that can translate between [Specific Languages] with high accuracy, considering context and nuances. It should also be able to act as a language tutor, providing lessons, exercises, and feedback on my progress."
IV. More Abstract & Challenging Agents:
Personalized Learning Path Generator: "Create an agent that designs a personalized learning path for me to acquire a specific skill or knowledge area (e.g., learn data science, learn to play the guitar). It should identify relevant resources (courses, books, tutorials), assess my current level, and adapt the plan based on my progress."
Ethical Decision-Making Advisor: "Develop an agent that can help me analyze ethical dilemmas by presenting different perspectives, identifying potential consequences, and suggesting relevant ethical frameworks. It should not make the decision for me, but provide information to support informed decision-making." (This is a very challenging and ethically sensitive prompt.)
Creative Problem Solver (Open-Ended): "Build an agent that can help me brainstorm solutions to open-ended problems. I will describe the problem, and the agent will generate a variety of potential approaches, considering different perspectives and unconventional ideas."
Social Media Content Curator & Scheduler: "Design an agent that can curate content relevant to my interests (or my business's target audience) for social media platforms (specify platforms, e.g., Twitter, Instagram, LinkedIn). It should suggest engaging captions, schedule posts for optimal times, and track engagement metrics."
Dream Interpreter: "Create an AI agent that can interpret my dreams. After being given the details of my dreams, the agent will use psychological and symbolic analysis to provide an interpretation."
These prompts are starting points. You'll likely need to refine them further based on the specific AI tools and techniques you plan to use (e.g., large language models, reinforcement learning, etc.). Remember to consider the ethical implications of your agent's actions and data usage. Good luck!
Okay, here are 20 prompts for creating AI agents, covering a range of complexities and applications. I've tried to include enough detail in each prompt to guide the agent's design and functionality, while still leaving room for creative interpretation. I've also categorized them roughly to help you find what you need.
I. Basic Utility & Information Agents (Easier to Implement):
Personalized News Curator: "Create an agent that learns my news preferences (topics, sources, writing style) over time and delivers a daily briefing of the most relevant and interesting articles, summarizing them concisely. It should prioritize credible sources and avoid clickbait. It should allow me to provide feedback on articles (e.g., 'more like this,' 'less like this')."
Smart Email Filter & Summarizer: "Design an agent that filters my email, prioritizing important messages, summarizing lengthy threads, flagging urgent requests, and identifying potential spam or phishing attempts with high accuracy. It should learn my communication patterns and adapt its filtering over time."
Meeting Scheduler: "Build an agent that can schedule meetings across multiple participants' calendars (Google Calendar, Outlook, etc.), considering time zones, preferred meeting times, and avoiding conflicts. It should be able to suggest optimal times based on availability and handle rescheduling requests intelligently."
Travel Planner: "Create an agent that can plan a trip based on my preferences (budget, destination type, activities, travel style). It should find flights, accommodation, and activities, and create a detailed itinerary. It should allow me to specify constraints (e.g., 'must have vegan food options,' 'no flights before 8 AM')."
Recipe Finder & Meal Planner: "Develop an agent that finds recipes based on available ingredients, dietary restrictions (vegetarian, gluten-free, etc.), and preferred cuisines. It should also be able to generate a weekly meal plan based on my preferences and create a shopping list."
II. Creative & Entertainment Agents (Moderate Complexity):
Story Co-writer: "Design an agent that collaborates with me to write stories. I will provide the initial premise and characters, and the agent will suggest plot points, dialogue, and descriptive passages. It should maintain a consistent style and tone, and allow me to guide the narrative direction."
Personalized Music Recommender & Playlist Generator: "Build an agent that recommends music based on my listening history, mood, and current activity (e.g., working, exercising, relaxing). It should be able to create playlists for specific occasions and discover new artists and genres that I might enjoy."
Art Style Transfer & Image Generator: "Create an agent that can take an input image and transform it into the style of a famous artist or a specified artistic style. It should also be able to generate original images based on text prompts, allowing for style and subject specifications."
Game Playing Agent (Specific Game): "Develop an agent that can play [Specific Game, e.g., Chess, Go, a specific video game]. It should learn strategies through self-play or by analyzing existing game data. Specify the level of difficulty desired (beginner, intermediate, expert)."
Poem Generator: "Create an agent that is capable of generating poems based on a user's input of a theme, style, and length. The agent should be able to understand the user's input and generate poems that are coherent, relevant, and aesthetically pleasing."
III. Specialized & Professional Agents (Higher Complexity):
Code Debugging & Refactoring Assistant: "Design an agent that can analyze code (specify language, e.g., Python, JavaScript) for bugs, suggest fixes, and propose refactoring improvements to enhance code readability, efficiency, and maintainability. It should explain its reasoning."
Customer Service Chatbot (Specific Industry): "Build a chatbot for [Specific Industry, e.g., e-commerce, banking, healthcare] that can handle common customer inquiries, resolve issues, and escalate complex problems to human agents. It should be able to understand natural language and maintain a professional and helpful tone."
Financial Advisor & Portfolio Manager: "Create an agent that can provide personalized financial advice based on my financial goals, risk tolerance, and current assets. It should be able to recommend investment strategies, track portfolio performance, and alert me to potential market risks."
Research Assistant (Specific Field): "Develop an agent that can assist with research in [Specific Field, e.g., biology, history, law]. It should be able to search academic databases, summarize relevant papers, and identify key findings and trends. It should cite sources accurately."
Language Translator & Tutor: "Design an agent that can translate between [Specific Languages] with high accuracy, considering context and nuances. It should also be able to act as a language tutor, providing lessons, exercises, and feedback on my progress."
IV. More Abstract & Challenging Agents:
Personalized Learning Path Generator: "Create an agent that designs a personalized learning path for me to acquire a specific skill or knowledge area (e.g., learn data science, learn to play the guitar). It should identify relevant resources (courses, books, tutorials), assess my current level, and adapt the plan based on my progress."
Ethical Decision-Making Advisor: "Develop an agent that can help me analyze ethical dilemmas by presenting different perspectives, identifying potential consequences, and suggesting relevant ethical frameworks. It should not make the decision for me, but provide information to support informed decision-making." (This is a very challenging and ethically sensitive prompt.)
Creative Problem Solver (Open-Ended): "Build an agent that can help me brainstorm solutions to open-ended problems. I will describe the problem, and the agent will generate a variety of potential approaches, considering different perspectives and unconventional ideas."
Social Media Content Curator & Scheduler: "Design an agent that can curate content relevant to my interests (or my business's target audience) for social media platforms (specify platforms, e.g., Twitter, Instagram, LinkedIn). It should suggest engaging captions, schedule posts for optimal times, and track engagement metrics."
Dream Interpreter: "Create an AI agent that can interpret my dreams. After being given the details of my dreams, the agent will use psychological and symbolic analysis to provide an interpretation."
These prompts are starting points. You'll likely need to refine them further based on the specific AI tools and techniques you plan to use (e.g., large language models, reinforcement learning, etc.). Remember to consider the ethical implications of your agent's actions and data usage. Good luck!
Okay, let's design an AI writing assistant agent. Since "assist me in my writing" is broad, I'll build a prompt that creates a versatile agent, covering several key writing assistance functions. I'll then break down how you might interact with this agent (the interaction model) and discuss some implementation considerations.
The Core Prompt:
"Create an AI agent called 'Scribo' (or a name you prefer) that acts as a comprehensive writing assistant. Scribo should be capable of the following functions, accessible through natural language commands and contextual understanding of the current document:
Grammar and Style Checking: Identify and correct grammatical errors, spelling mistakes, punctuation issues, and stylistic inconsistencies (e.g., passive voice overuse, repetitive phrasing, wordiness). Provide clear explanations for suggested changes and allow the user to accept, reject, or modify them. Offer multiple alternative phrasings.
Clarity and Conciseness Enhancement: Suggest improvements to sentence structure and word choice to make the writing clearer, more concise, and more impactful. Identify and help rephrase ambiguous or confusing passages.
Tone and Style Adjustment: Adapt the writing style and tone to a specified target audience or purpose (e.g., formal, informal, academic, persuasive, creative, technical). Allow the user to define a custom style guide (e.g., "write like Hemingway," or "use a confident and assertive tone").
Content Expansion and Idea Generation: Given a topic or a partially written piece, generate ideas for expanding the content, suggesting relevant examples, arguments, or supporting details. Offer different angles or perspectives on the topic. This should be done suggestively, not automatically inserting large blocks of text without user approval.
Research Assistance: When prompted (e.g., "find supporting evidence for this claim," "research the history of X"), conduct focused research using reliable sources (academic databases, reputable news outlets, etc.). Summarize relevant information and provide citations in a user-specified format (MLA, APA, Chicago, etc.).
Outlining and Structure: Help create outlines for new documents based on a user-provided topic or goal. Suggest logical structures and section headings. Analyze existing text and suggest improvements to its organization.
Rewriting and Paraphrasing: Rewrite selected sentences or paragraphs to improve clarity, change the tone, or avoid plagiarism. Offer multiple paraphrasing options.
Vocabulary Enhancement: Suggest alternative words or phrases to improve vocabulary and avoid repetition. Provide definitions and synonyms for selected words. Offer a "thesaurus" function.
Contextual Awareness: Maintain awareness of the entire document's content, style, and purpose. Ensure that suggestions are consistent with the overall context. Learn the user's writing style and preferences over time.
Fact-Checking: Flag potentially inaccurate or unsubstantiated claims and prompt the user to verify them.
Format and Citations: Assist in formating according to different style guidelines.
The agent should prioritize user control and transparency. It should explain its reasoning for suggestions and allow the user to easily accept, reject, or modify them. It should learn from user feedback and adapt its behavior over time."
Interaction Model (How You Would Use It):
There are several ways you could interact with this agent, depending on the implementation:
Text Editor Integration: The ideal scenario is integration within a text editor (like Google Docs, Microsoft Word, or a specialized writing application). This allows for:
Real-time Suggestions: As you type, Scribo could underline errors and offer suggestions in a sidebar or pop-up window (similar to Grammarly).
Contextual Menus: Right-clicking on a word or phrase could bring up options like "Paraphrase," "Find Synonyms," "Research this," "Check Tone," etc.
Overall Document Analysis: A dedicated panel could provide an overview of the document's readability, style consistency, and potential areas for improvement.
Command-Line Interface (CLI): A less user-friendly but still functional approach. You would interact with Scribo by typing commands:
scribo check grammar document.txt
scribo paraphrase "The quick brown fox jumps over the lazy dog."
scribo research "impact of AI on employment"
scribo outline "Benefits of renewable energy"
Chat Interface: You could interact with Scribo through a chat window (like interacting with ChatGPT):
You: "Can you check the grammar and style of this paragraph?" [Paste paragraph]
Scribo: "Here's the paragraph with suggested changes..." [Shows revised paragraph with explanations]
You: "Rewrite this sentence to be more formal."
Scribo: "Here are a few options..."
API Integration: For developers, an API would allow Scribo's functionality to be integrated into other applications.
Implementation Considerations:
Large Language Model (LLM): Scribo would almost certainly be built upon a powerful LLM (like those from OpenAI, Google, Anthropic, etc.). The LLM would handle the natural language understanding, generation, and many of the core writing tasks.
Fine-tuning: The base LLM would likely need to be fine-tuned on a large dataset of writing examples, grammar rules, style guides, and research data to specialize it for the assistant role.
Modular Design: Breaking down Scribo's functions into separate modules (grammar checker, style analyzer, research module, etc.) would make development and maintenance easier.
User Interface (UI): The UI is crucial for usability. A well-designed UI would make Scribo intuitive and efficient to use.
External APIs: Scribo might need to integrate with external APIs for tasks like:
Academic Databases: (e.g., JSTOR, PubMed, IEEE Xplore) for research.
Citation Management Tools: (e.g., Zotero, Mendeley) for handling citations.
Plagiarism Checkers: (e.g., Turnitin) for ensuring originality.
Ethical use: User should always take responsibility for the output produced, and ensure that AI generated writing is not passed off as their own without sufficient editing and citation.
Example Interactions (Assuming Text Editor Integration):
You type: "The affect of climate change is really bad."
Scribo underlines "affect" in red. A small pop-up appears: "Did you mean 'effect'? 'Affect' is usually a verb, meaning 'to influence.' 'Effect' is usually a noun, meaning 'a result.'" You click "Change to 'effect'."
You highlight a paragraph: "The company's profits increased significantly last quarter. This was due to a number of factors, including increased sales, reduced costs, and improved efficiency. The company is expected to continue to perform well in the future."
You right-click and select "Improve Clarity and Conciseness."
Scribo suggests: "The company's profits surged last quarter due to increased sales, reduced costs, and improved efficiency. Continued strong performance is anticipated." It highlights the changes.
You type: "Write a paragraph about the benefits of using solar energy."
Scribo will generate text such as, "Switching to solar power offers several advantages. Environmentally, it reduces reliance on fossil fuels, lowering carbon emissions and combating climate change. Economically, solar panels can significantly reduce or even eliminate electricity bills over time, providing long-term cost savings. Furthermore, solar energy increases energy independence, reducing vulnerability to fluctuating fuel prices and contributing to a more resilient energy grid."
You type: "I need to write an essay about the American Revolution. Help me create an outline."
Scribo presents a structured outline:
I. Introduction (Thesis statement: The American Revolution was caused by a complex interplay of political, economic, and social factors.)
II. Background: Colonial Grievances (e.g., Taxation without representation, British policies)
III. Key Events Leading to War (e.g., Boston Tea Party, Intolerable Acts)
IV. The War for Independence (e.g., Major battles, key figures)
V. Consequences and Legacy (e.g., Formation of the United States, impact on other nations)
VI. Conclusion
This detailed breakdown should give you a solid foundation for building or prompting for your AI writing assistant. Remember to iterate and refine the prompt based on the specific features and behavior you desire.
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Okay, let's design an AI writing assistant agent. To make this concrete and actionable, I'll outline the agent's features, functionalities, and then provide a series of prompts you could use (or adapt) to build it using various AI tools. I'll also discuss the underlying technologies that would likely be involved.
Agent Name: "Scribo" (You can, of course, choose a different name!)
Overall Goal: Scribo is a versatile AI writing assistant designed to help with various aspects of the writing process, from brainstorming and outlining to drafting, editing, and polishing. It aims to be adaptable to different writing styles and genres.
Key Features & Functionalities:
Brainstorming & Idea Generation:
Topic Suggestion: Suggests writing topics based on user-provided keywords, current events, or trending topics.
Prompt Generation: Creates writing prompts (e.g., story starters, essay questions) based on user-specified genre, theme, and length.
Mind Mapping: Helps create visual mind maps to explore ideas and connections related to a topic.
Character/Setting/Plot Development: Assists in developing characters (backstories, motivations, relationships), settings (descriptions, atmosphere), and plot points (conflicts, resolutions).
Outlining & Structure:
Outline Generation: Creates outlines for different types of writing (essays, articles, stories, reports) based on user input or a generated topic.
Section Suggestion: Suggests logical sections and sub-sections for a given topic and outline.
Outline Refinement: Allows the user to modify and refine the outline collaboratively.
Structure Analysis: Analyzes the structure of existing text and suggests improvements for clarity and flow.
Drafting & Content Generation:
Sentence/Paragraph Generation: Generates text based on the outline, user-provided keywords, or a specific writing style.
Content Expansion: Expands on existing sentences or paragraphs to add detail and depth.
Rewriting & Paraphrasing: Rewrites sentences or paragraphs in different styles or tones (e.g., formal, informal, concise, elaborate).
Synonym & Phrase Suggestion: Suggests alternative words and phrases to improve vocabulary and avoid repetition.
Transition Word/Phrase Suggestion: Suggests transition words and phrases to improve the flow and coherence of the text.
Editing & Proofreading:
Grammar & Spelling Check: Identifies and corrects grammatical errors, spelling mistakes, and punctuation issues.
Style & Tone Analysis: Analyzes the style and tone of the writing and suggests improvements for consistency and clarity.
Readability Assessment: Provides a readability score (e.g., Flesch-Kincaid) and suggests ways to improve readability.
Plagiarism Detection: Checks for potential plagiarism by comparing the text to online sources. (Requires integration with a plagiarism detection service).
Fact-Checking: Flags potentially inaccurate or unsubstantiated claims and suggests sources for verification. (Requires access to reliable knowledge bases).
Formatting & Presentation:
Style Guide Adherence: Helps format the text according to specific style guides (e.g., APA, MLA, Chicago).
Heading & Subheading Generation: Suggests appropriate headings and subheadings based on the content.
Citation Generation: Generates citations in various formats based on user-provided source information.
Learning & Personalization:
Style Preference Learning: Learns the user's preferred writing style, tone, and vocabulary over time.
Feedback Incorporation: Allows the user to provide feedback on suggestions (e.g., "good suggestion," "not helpful") to improve the agent's performance.
Customizable Settings: Allows the user to customize various settings, such as the level of formality, the target audience, and the desired length of the text.
Prompts for Building Scribo (Adaptable to Different AI Tools):
These prompts are designed to be used with large language models (LLMs) like GPT-3, GPT-4, Claude, Gemini, or similar. You might need to use different prompts for different parts of the agent. You can also chain prompts together (use the output of one prompt as input for another).
Brainstorming:
"Generate 10 creative writing prompts for a short story about [theme, e.g., time travel] in the [genre, e.g., science fiction] genre."
"I want to write an article about [topic]. Give me 5 potential angles or subtopics to explore."
"Develop three distinct character profiles for a [genre] story. Include their names, backstories, motivations, and potential conflicts."
Outlining:
"Create a detailed outline for an essay arguing [position] on the topic of [topic]."
"Given this outline: [paste outline], suggest three additional sub-sections for the section on [section topic]."
"Analyze the structure of this text: [paste text]. Identify any weaknesses in the organization and suggest improvements."
Drafting:
"Write an introductory paragraph for an article about [topic], using a [tone, e.g., formal and informative] tone."
"Expand on this sentence: [paste sentence]. Add more detail and description."
"Rewrite this paragraph in a more concise style: [paste paragraph]."
"Suggest three alternative phrases for the phrase '[phrase]' in this sentence: [paste sentence]."
Editing:
"Check this text for grammatical errors and spelling mistakes: [paste text]."
"Analyze the tone and style of this text: [paste text]. Is it consistent? Suggest improvements."
"What is the Flesch-Kincaid reading ease score of this text: [paste text]? How can I make it easier to read?"
"Find sentences that are too long within this text: [paste text]"
Formatting (More Rule-Based, Might Need Scripting):
(For citation generation, you'd likely need to use a specific library or API designed for this purpose. You could prompt an LLM to extract the necessary information, but the formatting itself is best handled by a dedicated tool.)
"Given this information: [Author, Title, Year, Publisher, etc.], generate a citation in APA format." (Then feed this to a citation formatting tool).
Learning & Personalization (Requires Data Storage & Retrieval):
This is the most complex aspect. You'd need to store user interactions, feedback, and writing samples. Prompts would be less about direct instruction and more about analyzing stored data. For example, you might analyze a user's writing samples to identify their typical sentence length, vocabulary choices, and stylistic preferences. Then, you'd use this information to tailor future suggestions.
Underlying Technologies:
Large Language Models (LLMs): The core of the agent. LLMs provide the natural language understanding and generation capabilities.
Natural Language Processing (NLP) Techniques: Specific NLP techniques like Named Entity Recognition (NER), Part-of-Speech (POS) tagging, dependency parsing, and sentiment analysis would be used for various tasks (e.g., identifying keywords, analyzing sentence structure, assessing tone).
Machine Learning (ML) Models: For personalization and learning, you'd likely need to train ML models on user data.
Data Storage: A database or other storage mechanism to store user preferences, writing samples, and feedback.
API Integrations: Integrations with external APIs (e.g., plagiarism detection services, grammar checking APIs, citation management tools) would be necessary for some features.
Implementation Steps (High-Level):
Choose Your Tools: Select the LLM(s) and other AI tools you'll use.
Design the User Interface: How will the user interact with Scribo? (e.g., text input, voice input, graphical interface).
Develop Individual Modules: Create separate modules for each feature (brainstorming, outlining
Okay, let's create a conceptual design for an AI writing assistant agent. I'll call it "Wordsmith." This will cover its features, how it might work, and example prompts you could use to build it (or parts of it) with existing AI tools. Since "help me write" is broad, I'll cover a range of functionalities, from basic assistance to more advanced collaborative writing.
Agent Name: Wordsmith
Overall Goal: Wordsmith is an AI-powered writing companion designed to assist users in all stages of the writing process, from initial brainstorming to final polishing. It aims to be versatile, adapting to different writing styles, genres, and user needs. It's not meant to replace the writer, but to be a powerful tool to enhance creativity, productivity, and the quality of written work.
Key Features and Functionalities:
Idea Generation & Brainstorming:
Topic Suggestions: Based on keywords, current events, or user interests, Wordsmith suggests potential writing topics.
Prompt Generation: Creates writing prompts (story starters, essay questions, article ideas) tailored to specific genres, themes, and lengths.
Content Outlining: Helps users structure their ideas by generating outlines for different types of writing (essays, stories, reports, etc.).
Character/Setting/Plot Development (for fiction): Assists in creating detailed character profiles, describing settings, and developing plot points and conflicts.
Mind Mapping (Conceptual): While Wordsmith might not directly create visual mind maps, it can generate the content for a mind map, which could then be visualized using a separate tool.
Drafting Assistance:
Sentence Starters & Completions: Provides suggestions for starting or completing sentences based on the context.
Paragraph Generation: Generates paragraphs based on keywords, outlines, or existing text.
Content Expansion: Expands on short pieces of text (sentences, paragraphs) to add detail and depth.
Rewriting & Paraphrasing: Rewrites text in different styles (formal, informal, concise, elaborate) or paraphrases to avoid repetition or improve clarity.
Vocabulary Enhancement: Suggests synonyms, alternative phrases, and more precise wording.
Transition Assistance: Provides appropriate transition words and phrases to improve the flow and coherence of the text.
Editing & Proofreading:
Grammar & Spelling Check: Identifies and corrects grammatical errors, spelling mistakes, and punctuation issues.
Style & Tone Analysis: Analyzes the writing style and tone, suggesting improvements for consistency, clarity, and audience appropriateness.
Readability Assessment: Provides a readability score (e.g., Flesch-Kincaid) and suggests ways to improve readability.
Repetition Detection: Highlights repetitive words and phrases.
Passive Voice Detection: Identifies instances of passive voice and suggests alternatives (where appropriate).
Sentence Structure Analysis: Identifies overly long or complex sentences and suggests ways to simplify them.
Research & Information Gathering (Light):
Fact Suggestion: While not a primary research tool, Wordsmith can suggest potential facts or statistics related to the topic (to be verified by the user).
Definition Lookup: Provides definitions of words and terms.
Source Suggestion (Limited): Can suggest potential sources for further research, but the user should always verify their credibility.
Learning & Personalization:
Style Learning: Adapts to the user's preferred writing style, tone, and vocabulary over time.
Feedback Incorporation: Allows the user to provide feedback on suggestions (e.g., "helpful," "not helpful," "too formal") to improve Wordsmith's performance.
Customization: Allows users to set preferences for writing style, target audience, and other parameters.
Example Prompts (for use with LLMs like GPT-3, GPT-4, Claude, Gemini, etc.):
These prompts are illustrative and can be adapted/combined. You'll likely need to experiment to get the best results. The success of the agent depends heavily on the capabilities of the underlying LLM.
Idea Generation:
"Give me 5 writing prompts for a short story in the fantasy genre, focusing on a young mage discovering their powers."
"I want to write an article about sustainable living. Suggest three potential subtopics I could focus on."
"Generate an outline for a 5-paragraph essay arguing for the importance of [topic]."
Drafting:
"Complete this sentence: 'The old house stood on a hill overlooking...'"
"Write a paragraph describing a bustling marketplace in a fantasy city."
"Expand on this sentence: 'The experiment yielded unexpected results.' Add details about the experiment and the results."
"Rewrite this sentence in a more formal style: 'The guy was really mad.'"
"Suggest three synonyms for the word 'important'."
"What are some transition words I can use to connect these two paragraphs: [paste paragraphs]?"
Editing:
"Check this text for grammar and spelling errors: [paste text]"
"Analyze the tone of this paragraph: [paste paragraph]. Is it appropriate for a formal academic paper?"
"Identify any instances of passive voice in this text: [paste text]"
"Are there any sentences in this text that are too long or complex: [paste text]?"
"Suggest ways to improve the readability of this paragraph: [paste paragraph]"
Research (light):
* What is the definition of "photosynthesis"?
* Suggest facts about deforestation to input into this paragraph: [paste paragraph]Learning/Personalization: (This is the most complex aspect and would require a more sophisticated system than just prompting an LLM. It would involve data storage and retrieval, and likely some form of machine learning to analyze user data.)
Underlying Technologies:
Large Language Models (LLMs): The core engine for understanding and generating text.
Natural Language Processing (NLP): Various NLP techniques (tokenization, part-of-speech tagging, dependency parsing, named entity recognition, sentiment analysis) would be used to analyze and process text.
User Interface: A way for the user to interact with Wordsmith (text input, voice input, a graphical interface).
Data Storage (for personalization): A way to store user preferences, writing samples, and feedback.
Potentially: API Integrations: For features like plagiarism detection or advanced grammar checking, integration with external APIs might be useful.
Implementation Steps (High-Level):
Choose your LLM(s) and other tools.
Design the User Interface: How will the user interact with the agent?
Develop Modules: Create separate modules for each functionality (brainstorming, drafting, editing, etc.).
Prompt Engineering: Carefully craft prompts for each module to get the desired behavior from the LLM.
Iterative Refinement: Test the agent extensively and refine the prompts and modules based on user feedback.
Consider Personalization: If you want personalization, you'll need to design a system for collecting and using user data.
This provides a comprehensive overview. Building a fully functional AI writing assistant like Wordsmith is a significant undertaking, but you can start by building individual components and gradually expanding its capabilities. Remember to always critically evaluate the AI's output and to use it as a tool to enhance your own writing, not to replace it
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Okay, let's design an AI agent to help develop an integrated program for high school student scheduling, grade reporting, and transcript maintenance. This is a complex, multi-faceted project, so the AI agent will primarily assist in planning, designing, and generating code snippets, rather than building the entire system single-handedly. We'll call the agent "EduPlanner."
Agent Name: EduPlanner
Overall Goal: EduPlanner assists in the design, development, and implementation of a comprehensive, integrated software system for managing high school student schedules, grades, and transcripts. It leverages AI to provide suggestions, generate code, analyze requirements, and ensure best practices are followed. It's a development assistant, not an autonomous developer.
Key Features and Functionalities (of the target system, with EduPlanner's assistance):
Student Information Management:
Student Profiles: Store student demographic data, contact information, enrollment history, etc. (EduPlanner: Suggest database schema, data validation rules).
Course Catalog: Manage a database of courses, including descriptions, prerequisites, credit hours, and teacher assignments. (EduPlanner: Generate sample data, suggest categorization schemes).
Enrollment Management: Allow students (or administrators) to enroll in courses, manage waitlists, and handle course drops/adds. (EduPlanner: Design user interface flow, suggest algorithms for conflict detection).
Scheduling:
Master Schedule Generation: Create a master schedule that optimizes course offerings, teacher availability, and classroom utilization. (EduPlanner: Suggest algorithms for schedule optimization – genetic algorithms, constraint satisfaction).
Student Schedule Creation: Assign individual students to courses based on their requests, prerequisites, and the master schedule. (EduPlanner: Generate code snippets for conflict resolution, suggest data structures for representing schedules).
Schedule Modification: Allow administrators to make changes to the master schedule and individual student schedules. (EduPlanner: Design user interface for schedule editing, suggest validation rules to prevent conflicts).
Grade Reporting:
Grade Input: Provide a user-friendly interface for teachers to enter grades for assignments, quizzes, and exams. (EduPlanner: Suggest input validation rules, design gradebook layout).
Grade Calculation: Calculate weighted averages, GPAs, and other relevant metrics. (EduPlanner: Generate code for grade calculation formulas, handle different grading scales).
Progress Reports: Generate progress reports for students and parents, showing current grades and performance. (EduPlanner: Suggest report templates, design data visualization options).
Grade Analysis: Provide tools for analyzing grade distributions, identifying at-risk students, and tracking academic progress. (EduPlanner: Suggest statistical analysis methods, data visualization techniques).
Transcript Management:
Transcript Generation: Create official transcripts that include student information, course history, grades, GPA, and graduation status. (EduPlanner: Suggest transcript formats, ensure compliance with relevant standards).
Transcript Request & Delivery: Allow students (or alumni) to request transcripts and securely deliver them to colleges or other institutions. (EduPlanner: Design workflow for transcript requests, suggest secure data transmission methods).
Credit Tracking: Track student progress towards graduation requirements. (EduPlanner: Generate code for credit calculation, suggest user interface for displaying progress).
User Roles & Permissions:
Administrators: Full access to all system features.
Teachers: Access to grade input, student roster management, and their own schedule.
Students: Access to their own schedule, grades, and transcript request functionality.
Parents/Guardians: Access to their child's schedule, grades, and progress reports.
(EduPlanner: Design user role management system, generate code snippets for access control).
Reporting & Analytics:
Customizable Reports: Allow administrators to generate reports on various aspects of the system (e.g., enrollment statistics, grade distributions, student performance).
Data Visualization: Provide charts, graphs, and other visualizations to help understand data trends.
(EduPlanner: Suggest reporting tools and libraries, generate code for data aggregation and visualization).
System requirements:
Determine necessary system requirements.
Set up testing, integration, and deployment.
(EduPlanner: provide standard practices, libraries, suggestions, and frameworks for testing and deployment)
EduPlanner's Assistance (Example Prompts and Interactions):
These prompts would be used with an LLM or a combination of AI tools. The output would be suggestions, code snippets, and design ideas, not a fully working system.
Database Design:
"Suggest a database schema (tables and fields) for storing student information, including demographics, contact details, and enrollment history. Use a relational database model (e.g., PostgreSQL)."
"Generate SQL code to create the 'Students' table based on this schema: [paste schema]."
"What are some best practices for data validation when storing student addresses in a database?"
Scheduling Algorithm:
"I need an algorithm to generate a master schedule for a high school. Suggest some approaches, considering constraints like teacher availability, classroom capacity, and student course requests. Prioritize minimizing student schedule conflicts."
"Explain the pros and cons of using a genetic algorithm for school schedule optimization."
"Generate Python code to represent a student's schedule as a data structure. Consider courses, time slots, and days of the week."
"Generate Python code to represent a class schedule conflict."
Grade Calculation:
"Generate JavaScript code to calculate a weighted average grade, given an array of assignments with scores and weights."
"How can I handle different grading scales (e.g., letter grades, percentages, 4.0 scale) in a grade calculation system? Suggest a data structure to represent grading scales."
User Interface Design:
"Suggest a user interface layout for a teacher's gradebook, allowing them to enter grades for multiple students and assignments. Consider usability and accessibility."
"What are some best practices for designing a user interface for mobile devices?"
Security:
"What are some important security considerations for a web application that handles sensitive student data?"
"Suggest methods for securely storing passwords in a database."
"How can I implement role-based access control in a web application using [framework, e.g., Django, React]?"
System Requirements, Testing, and Deployment:
`"Outline standard practices for unit testing in [framework, e.g., Django, React]"
"Outline a testing plan for the integrated program."
"What are some best practices for integrating different modules in a software program?"
"Compare these deployment options for an integrated software program, and create a suggested framework for deployment: [list of deployment methods]"
Underlying Technologies (for both EduPlanner and the target system):
EduPlanner:
Large Language Models (LLMs): For code generation, design suggestions, and answering questions.
Code Analysis Tools: To analyze and understand code snippets.
Knowledge Base: Access to information about software development best practices, algorithms, and educational standards.
Target System:
Programming Languages: Python (with Django or Flask), JavaScript (with React, Angular, or Vue.js), Java, etc.
Database: PostgreSQL, MySQL, MongoDB (depending on the chosen data model).
Web Framework: Django, Flask, React, Angular, Vue.js, etc.
Cloud Platform (Optional): AWS, Azure, Google Cloud (for hosting and scalability).
Security Libraries & Frameworks: For authentication, authorization, and data encryption.
Testing Frameworks: Pytest, Jest, Mocha, etc.
Reporting Libraries: For generating reports and visualizations.
Implementation Steps (High-Level):
Requirements Gathering: Use EduPlanner to help define detailed requirements for the system.
System Design: Use EduPlanner to assist in designing the database schema, user interface, and overall architecture.
Module Development: Develop individual modules (scheduling, grading, transcripts) separately, using EduPlanner for code generation and problem-solving.
Integration: Integrate the modules into a cohesive system.
Testing: Thoroughly test the system, including unit tests, integration tests, and user acceptance testing. Use EduPlanner to help create test cases.
Deployment: Deploy the system to a suitable environment (e.g., a school server or a cloud platform).
Documentation: Write thorough documentation to ensure easy use.
This is a substantial project. EduPlanner would be a valuable assistant throughout the process, but significant software development expertise is still required. The key is to break down the problem into smaller, manageable tasks and use EduPlanner to assist with each step. Remember to always critically evaluate the AI's output and to adapt it to your specific needs and context
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Okay, let's design an AI agent for researching Bible verses, including etymology, semantic analysis, and outline generation. We'll call it "ScripturaScholar."
Agent Name: ScripturaScholar
Overall Goal: ScripturaScholar is an AI agent designed to provide in-depth research and analysis of Bible verses. It goes beyond simple lookups, offering etymological information, semantic analysis within the original languages (primarily Hebrew and Greek), and contextual understanding, culminating in a structured outline summarizing its findings.
Key Features and Functionalities:
Verse Input & Identification:
Accepts Verse References: The user provides a Bible verse reference (e.g., "John 3:16," "Genesis 1:1," "Psalm 23").
Handles Multiple Translations: Allows the user to specify a preferred Bible translation (e.g., NIV, KJV, ESV, NASB) or compare multiple translations.
Verse Extraction: Retrieves the text of the verse from a chosen translation (likely using an API or local database of Bible texts).
Etymological Analysis:
Identifies Key Words: Determines the significant words within the verse (nouns, verbs, adjectives).
Traces Word Origins: For each key word, it traces its etymology back to the original Hebrew (Old Testament) or Greek (New Testament) word.
Provides Root Meanings: Explains the root meaning of the original language word and its historical development.
Lexical Resources: Integrates with (or emulates) lexical resources like Strong's Concordance, Thayer's Greek Lexicon, Brown-Driver-Briggs Hebrew Lexicon.
Semantic Analysis:
Original Language Context: Analyzes the word's usage in the original language within the specific context of the verse.
Range of Meanings: Explores the range of possible meanings of the word in the original language, considering its grammatical form and context.
Cross-References: Identifies other verses in the Bible where the same original language word is used, providing insights into its broader semantic range.
Figurative Language Detection: Attempts to identify and interpret figures of speech (metaphors, similes, hyperbole, etc.) based on the original language context.
Contextual Analysis:
Immediate Context: Examines the verses immediately preceding and following the target verse to understand the flow of thought.
Book Context: Considers the overall theme and purpose of the book in which the verse is found.
Historical & Cultural Context: Provides (briefly) relevant historical and cultural background information that might shed light on the verse's meaning.
Theological Context: (Optionally) Identifies relevant theological themes and connections to other parts of Scripture. This is the most challenging and potentially controversial aspect.
Outline Generation:
Structured Summary: Creates a well-organized outline summarizing the findings of the etymological, semantic, and contextual analyses.
Hierarchical Structure: Uses a hierarchical structure (I. Main Point, A. Subpoint 1, B. Subpoint 2, etc.) to organize the information logically.
Clear Headings: Provides clear and concise headings for each section of the outline.
Example Prompts and Interactions (using an LLM like GPT-4, Claude, etc., plus specialized tools):
This agent will require a combination of LLM capabilities and integration with specialized resources (Bible APIs, lexicons).
Input: "Analyze John 3:16 in the NIV."
Verse Extraction (likely using an API):
"Retrieve the text of John 3:16 from the NIV translation." (This would likely be an API call to a Bible API).
Etymological Analysis:
"What are the key words in John 3:16 (Greek text)?" (The agent would need access to the Greek text).
"What is the Greek word for 'loved' in John 3:16? Provide its Strong's number and a brief definition."
"Trace the etymology of the Greek word [Greek word for 'loved']. What is its root meaning?"
"What is the range of meaning of agapao (ἀγαπάω) in Koine Greek?"
Semantic Analysis:
"How is the word 'agapao' (ἀγαπάω) used in other verses in the Gospel of John? Provide examples."
"What is the grammatical form of 'agapao' (ἠγάπησεν) in John 3:16? What does this indicate about its meaning in this context?"
"Is there any figurative language used in John 3:16 in the original Greek?"
Contextual Analysis:
"Summarize the verses immediately before and after John 3:16. What is the context of this verse?"
"What is the main theme of the Gospel of John?"
"What relevant historical or cultural information might help understand John 3:16?"
Outline Generation:
"Create an outline summarizing the etymological, semantic, and contextual analysis of John 3:16 (NIV). Include sections for: Verse Text, Key Words & Etymology, Semantic Range, Context (Immediate, Book, Historical), and Theological Significance (optional)."
Underlying Technologies:
Large Language Models (LLMs): For natural language understanding, text generation, and synthesizing information.
Bible APIs: For retrieving verse text, accessing different translations, and potentially accessing lexical data. (e.g., Bible Gateway API, ESV API, etc.).
Lexical Databases: Access to digital versions of Strong's Concordance, Thayer's Greek Lexicon, Brown-Driver-Briggs Hebrew Lexicon, or similar resources. This could be through APIs or local databases.
NLP Techniques: Part-of-speech tagging, dependency parsing, named entity recognition (to identify key words and grammatical relationships).
Knowledge Representation: A way to represent the relationships between words, concepts, and verses (e.g., a knowledge graph).
Data Storage: A database to store cached data (verse texts, lexical information) to improve performance.
Implementation Steps (High-Level):
Data Acquisition: Set up access to Bible APIs and lexical databases.
Verse Processing Module: Develop a module to handle verse input, translation selection, and text retrieval.
Etymological Analysis Module: Implement functionality to identify key words, access lexical data, and trace word origins.
Semantic Analysis Module: Develop algorithms for analyzing word usage in context, identifying cross-references, and detecting figurative language.
Contextual Analysis Module: Create functionality to analyze the immediate, book, and historical/cultural context of the verse.
Outline Generation Module: Design an algorithm to create structured outlines based on the analysis results.
Integration & User Interface: Combine the modules and create a user interface for interacting with ScripturaScholar.
Testing & Refinement: Thoroughly test the agent with a wide range of verses and refine its performance based on feedback.
Challenges and Considerations:
Ambiguity: Biblical interpretation can be subjective. ScripturaScholar should present multiple possible interpretations where appropriate and avoid making definitive theological pronouncements.
Figurative Language: Accurately identifying and interpreting figurative language is a challenging NLP task.
Theological Bias: The agent should be designed to be as objective as possible, but it's important to be aware of potential biases that might be embedded in the underlying data or algorithms. Transparency is crucial.
Accuracy of Lexical Data: The accuracy of the agent depends on the quality and accuracy of the underlying lexical resources.
ScripturaScholar would be a powerful tool for Bible study, providing a deeper understanding of the original languages and context of Scripture. However, it should be used as a tool for exploration and understanding, not as a definitive interpreter of the Bible. Human judgment and critical thinking are always essential.
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Okay, let'Okay, let's design an AI agent for researching a topic, summarizing the findings, creating an outline, and generating a 20-question quiz with an answer key. Wes design an AI agent that can research a topic, summarize the findings, create an outline, and generate a 20-question quiz with an answer key.'ll call this agent "ResearchPro."
Agent Name: ResearchPro
Overall Goal: ResearchPro is an AI-powered research assistant designed to help users quickly We'll name it "Inquisitor."
Agent Name: Inquisitor
Overall Goal: Inquisitor is an AI agent designed to facilitate learning and understanding of a given topic. It automates the process of research, summarization, outlining, and assessment creation. It aims to be a versatile gain a comprehensive understanding of a given topic. It performs research, summarizes key information, structures the information into an outline, and generates a quiz to test comprehension.
**Key tool for students, educators, and anyone seeking to quickly grasp a subject.
Key Features and Functionalities:
Topic Input & Scope Definition:
User-Provided Topic: The user provides a topic Features and Functionalities:**
Topic Input & Refinement:
Accepts Broad or Specific Topics: The user can input a broad topic (e.g., "climate change") or a more specific (e.g., "The French Revolution," "Photosynthesis," "Quantum Computing").
Scope Refinement (Optional): The user can optionally specify the scope of the research (e.g., "focus on the causes one (e.g., "the impact of rising sea levels on coastal communities").
Topic Clarification: If the topic is too broad, ResearchPro might ask clarifying questions to narrow the scope or suggest subtopics.
," "limit to the 18th century," "explain in simple terms").
Research & Information Gathering:
Multi-Source Research: Gathers information from a variety of sources, including reputable websites, academic databases (if accessible), and online encyclopedias.
*** Search Scope Definition: Allows the user to specify the desired depth of research (e.g., overview, in-depth analysis) and the types of sources to consider (e.g., academic papers, news articles, reputable websites).
Information Gathering & Synthesis:
WebSource Credibility Assessment: Attempts to prioritize credible and reliable sources. This is a critical and challenging aspect.
Data Extraction: Extracts relevant information (facts, concepts, definitions, examples) from the sources.
Summarization:
Concise Summary Generation Search: Performs web searches using multiple search engines (e.g., Google, Bing, DuckDuckGo) to gather relevant information.
Source Evaluation: Evaluates the credibility and reliability of sources, prioritizing reputable websites, academic publications, and expert opinions. It should be able to distinguish between primary:** Creates a concise summary of the research findings, highlighting key points and concepts.
Multiple Summary Lengths (Optional): Can generate summaries of different lengths (e.g., short, medium, long) based on user preference.
Key Concept Identification: Identifies and emphasizes and secondary sources.
Information Extraction: Extracts key facts, concepts, arguments, and data from the sources.
Data Synthesis: Combines information from multiple sources, identifying common themes, conflicting viewpoints, and areas of uncertainty.
Summary Generation:
**Conc the most important concepts and terms related to the topic.
Outline Generation:
Hierarchical Outline: Creates a well-structured outline organizing the research findings into a logical hierarchy (main topics, subtopics, supporting details).
Outline Customization: Allows the user to modifyise Overview:** Creates a concise summary of the topic, covering the main points and key findings.
Multiple Summaries (Optional): Can generate summaries of different lengths (e.g., short, medium, long) to suit different needs.
Key Term Definitions: Defines important and refine the outline.
Quiz Generation:
Multiple-Choice Questions: Generates 20 multiple-choice questions based on the research findings.
Answer Key Generation: Provides the correct answer for each question.
Difficulty Levels (Optional): Can terms and concepts related to the topic.
Outline Generation:
Hierarchical Structure: Creates a well-organized outline of the topic, using a hierarchical structure (main headings, subheadings, bullet points).
Logical Flow: Ensures that the outline follows a logical flow create questions of varying difficulty levels (e.g., easy, medium, hard).
Question Variety: Strives for a variety of question types (e.g., factual recall, conceptual understanding, application).
Source Citation:
Include a list of URLs which were used for the prompt, presenting information in a clear and coherent manner.
Customizable Outline: Allows the user to modify and refine the outline.
Quiz Generation:
Multiple-Choice Questions: Generates 20 multiple-choice questions based on the research findings.
Example Prompts and Interactions (using an LLM and other tools):
This agent would likely involve a combination of LLM capabilities and integration with search engines and potentially academic databases.
Input:
`"Research the topic: 'The American Civil War'. Focus on the causes Variety of Question Types: Includes different types of questions (e.g., factual recall, comprehension, application) to assess different levels of understanding.
Distractor Generation: Creates plausible but incorrect answer choices (distractors) for each question.
Answer Key: Provides a corresponding and major battles."`
Research (Combination of LLM and Search API):
"Search for information on the causes of the American Civil War." (This would be translated into a search engine query).
`"Search for information on the major battles of the American answer key with the correct answer for each question.
Difficulty Levels (Optional) : Creates quizzes of variable difficulty levels.
Example Prompts and Interactions (using an LLM and other tools):
Input: "Research the causes and effects of the American Civil War."
Civil War."`
* The agent would then extract relevant information from the search results, prioritizing reputable sources.Summarization:
"Summarize the main causes of the American Civil War in 200 words."
"List the five most important battles of the American Civil War and briefly describe each one."
Outline Generation:
"Create a detailed outline on the topic of 'The American Civil War', including sections on causes, major battles, key figures, and consequences."
Quiz Generation:
Scope Refinement (if needed): "Would you like to focus on the political, social, or economic causes and effects? Or all of them?"Information Gathering: (This would involve multiple web searches and data extraction steps, not easily represented by single prompts)
Summary Generation "Generate a 20-question multiple-choice quiz on the American Civil War, covering the causes, major battles, and key figures. Provide the correct answers."
* (Example Question) `"What was the primary cause of the American Civil War?
a) Economic differences:*"Summarize the main causes of the American Civil War in 300 words."
"Summarize the major consequences of the American Civil War in 500 words."
"Define the following terms related to the American Civil between the North and South b) The issue of slavery c) States' rights d) All of the above"
Source Citation:
"Create a list of URLs which were used for the research in the prompt"
Underlying Technologies:
War: secession, abolitionism, Emancipation Proclamation."`
Outline Generation:
`"Create an outline for a research paper on the American Civil War. Include sections on: Introduction, Background, Causes (Political, Social, Economic), Major Events, Consequences (Short-term, Long* Large Language Models (LLMs): For text generation, summarization, question answering, and outline creation.
Search APIs: (e.g., Google Search API, Bing Search API) For retrieving information from the web.
Web Scraping (Potentially): For-term), and Conclusion."`
Quiz Generation:
"Generate 5 multiple-choice questions about the causes of the American Civil War. Include an answer key."
`"Generate 5 multiple choice questions about the effects of the American Civil War. Include an extracting data from specific websites (requires careful handling of robots.txt and terms of service).
Natural Language Processing (NLP): Techniques like named entity recognition, keyword extraction, and text summarization.
Data Storage: To store the research findings, summaries, outlines, and quizzes.
Implementation answer key."* "Generate 5 multiple-choice questions about the key figures and events of the American Civil War. Include an answer key."* "Generate 5 multiple-choice questions testing comprehension of the social context surrounding the American Civil War. Include an answer key." Steps (High-Level):
Topic Input & Processing: Create a module to handle user input and define the research scope.
Research Module: Develop a module to interact with search APIs and potentially scrape web pages. Implement source credibility assessment (this is challenging).
`"Create a multiple-choice question about [specific topic from the research]. Include four answer choices, one of which is correct. Provide the correct answer."
Underlying Technologies:
Large Language Models (LLMs): For natural language understanding, text generation,Summarization Module: Utilize LLM capabilities for text summarization.
Outline Generation Module: Employ LLM and NLP techniques to create structured outlines.
Quiz Generation Module: Develop algorithms to generate multiple-choice questions and answers based on the research findings.
Integration & User Interface: Combine the modules and create a user interface.
Testing & Refinement: Thoroughly test the agent and refine its performance.
Challenges and Considerations:
Source Credibility: Ensuring the reliability of information sources is crucial and difficult. The agent should prioritize reputable sources and potentially summarization, and question generation.
Web Search APIs: To access search engine results (e.g., Google Custom Search API, Bing Web Search API).
Web Scraping Tools: To extract content from web pages (e.g., Beautiful Soup, Scrapy). *Use responsibly provide warnings about less credible ones.
Bias: The agent might inherit biases from the data it is trained on or from the sources it retrieves information from.
Factual Accuracy: LLMs can sometimes generate incorrect or misleading information ("hallucinations"). Cross-referencing information from multiple sources is essential and ethically, respecting website terms of service.*
Natural Language Processing (NLP): For tasks like named entity recognition, keyword extraction, sentiment analysis (to identify key concepts and relationships).
Data Storage: To store research findings, summaries, outlines, and quizzes.
**Knowledge.
Scope Management: Keeping the research focused on the specified topic and scope can be challenging.
Question Quality: Generating high-quality, relevant, and unambiguous quiz questions requires careful prompt engineering and potentially some form of question filtering.
Inquisitor would be a valuable tool for learning and assessment Graph (Optional):** To represent relationships between concepts and entities related to the topic.
Implementation Steps (High-Level):
Topic Input & Refinement Module: Develop the interface for accepting and clarifying user input.
Information Gathering Module: Implement web search, source evaluation, and. However, it's important to emphasize that it's an assistant, not a replacement for critical thinking and human judgment. Users should always verify the information provided by the agent and consult multiple sources. The agent's output should be treated as a starting point for further exploration, not as the definitive truth.
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