Prompt Engineering Vs. 6

 

 Prompt engineering is a crucial skill for effectively interacting with and utilizing large language models (LLMs). It involves designing, refining, and optimizing prompts to elicit desired responses from these AI models. A variety of tools and techniques have emerged to aid in this process, enhancing the performance, reliability, and capabilities of LLMs.

Key Prompt Engineering Techniques

Several techniques are employed to guide LLMs toward generating more accurate, relevant, and complex outputs. These include:

• Zero-Shot Prompting: This technique involves instructing the LLM to perform a task without providing any prior examples in the prompt. The model relies solely on its pre-existing knowledge and the clarity of the instruction. For instance, asking a model to "Classify the following text as neutral, negative, or positive: 'I think the vacation was okay.'"
• Few-Shot Prompting: In this approach, a few examples of the desired input and output are included in the prompt. This helps the model understand the task and context better, especially for more complex tasks. For example, providing pairs of sentences and their sentiment before asking the model to classify a new sentence.
• Chain-of-Thought (CoT) Prompting: This method encourages the LLM to break down complex reasoning tasks into a series of intermediate steps, mimicking a thought process. Often, simply adding "Let's think step by step" to a prompt can trigger this behavior and lead to more accurate results, especially for arithmetic, commonsense, and symbolic reasoning tasks.
• Prompt Chaining: This technique involves breaking down a complex task into a sequence of simpler prompts. The output of one prompt becomes the input for the next, creating a chain that guides the LLM's response generation process.
• Tree of Thoughts (ToT): This more advanced technique allows LLMs to explore multiple reasoning paths simultaneously. The model can generate various intermediate thoughts, evaluate them, and use search algorithms (like breadth-first or depth-first search) to navigate the "tree" of thoughts to find the most promising solution.
• Generated Knowledge Prompting: This involves first asking the LLM to generate relevant facts or knowledge about a topic and then using this generated information within the prompt to answer a specific question or complete a task.
• Least-to-Most Prompting: This strategy involves structuring prompts to gradually increase in complexity. It starts with simpler sub-problems and uses the solutions to those to help solve more complex parts of the overall problem.
• Self-Consistency: This technique aims to improve the reliability of LLM outputs by generating multiple responses to the same prompt using a more diverse decoding strategy (e.g., by increasing the temperature parameter). The most consistent answer among these diverse outputs is then selected.
• Retrieval Augmented Generation (RAG): RAG combines the generative power of LLMs with information retrieval from external knowledge sources. When faced with a prompt, the system first retrieves relevant documents or data snippets and then uses this information to augment the context provided to the LLM, leading to more factual and up-to-date responses.
• Active-Prompt: This technique involves iteratively refining prompts based on the model's initial responses. It often uses uncertainty estimation or other signals from the model to identify areas where the prompt needs improvement or where more specific examples are required.
• Directional Stimulus Prompting: This focuses on guiding the AI's attention to specific aspects of the input or desired output, helping to enhance the relevance and focus of the generated response.
• Maieutic Prompting: This technique, inspired by the Socratic method, prompts the model to explain its reasoning or the steps it took to arrive at an answer. This can help in understanding the model's "thought process" and identifying inconsistencies.
• Graph Prompting: This involves integrating knowledge graphs into the prompting process to improve the relevance and quality of responses by providing structured contextual information.

Best Practices in Prompt Engineering

Beyond specific techniques, several best practices contribute to effective prompt engineering:

• Be Specific and Clear: Provide precise instructions and avoid ambiguity. Clearly define the desired output, format, length, tone, and target audience.
• Provide Sufficient Context: Include relevant background information, examples, and constraints to guide the model.
• Use Structured Formats: Employ headings, bullet points, numbered lists, or templates to organize prompts and guide the AI's response structure.
• Leverage Role-Playing: Ask the LLM to adopt a specific persona or role (e.g., "Act as an expert physicist") to tailor its responses.
• Specify Output Constraints: Clearly define the desired format (e.g., JSON, XML, list, table) and any constraints on the output.
• Instruct What to Do, Not What Not to Do: Frame instructions positively. For example, instead of "Don't use technical jargon," use "Explain this in simple terms."
• Iterate and Experiment: Prompt engineering is often an iterative process. Test different phrasings, techniques, and parameters to find what works best for a given task and model.
• Understand Model Limitations: Be aware of the knowledge cutoff dates, potential biases, and inherent limitations of the LLM you are using.

Tools for Prompt Engineering

A growing ecosystem of tools is available to assist developers and researchers in crafting, managing, evaluating, and deploying prompts:

• LangChain: An open-source framework designed to simplify the creation of applications using LLMs. It provides modules for managing prompts, chaining LLM calls, incorporating memory, indexing data, and creating agents.
• PromptLayer: A platform for tracking, managing, and versioning prompts. It allows users to log requests, view prompt history, and collaborate on prompt development.
• OpenAI Playground: An interactive web-based tool that allows users to experiment with OpenAI's language models in real-time, adjust parameters, and test different prompts.
• Prompt Engine: An open-source (NPM utility library, primarily TypeScript) tool for developing and maintaining LLM prompts.
• Agenta: An open-source platform for experimenting with, evaluating, and deploying LLMs, enabling developers to test multiple prompt variations and models.
• OpenPrompt: A library (built on PyTorch) that provides a standardized framework for prompt-learning, allowing users to adapt pre-trained language models to downstream NLP tasks.
• PromptHub: An AI-driven tool focused on generating high-quality prompts, offering features for prompt optimization and templates.
• PromptAppGPT: A user-friendly, low-code platform that simplifies prompt creation, potentially with drag-and-drop interfaces, and supports various generation tasks.
• Promptmetheus: An all-inclusive Integrated Development Environment (IDE) specifically designed for prompt engineering with LLMs.
• PromptPerfect: A tool aimed at improving prompt quality to achieve consistent results from LLMs, allowing deployment of prompts to its server.
• Dust.tt: A platform that helps build LLM applications as a series of prompted calls to external models, offering a graphical UI for building prompt chains.
• BetterPrompt: A tool or test suite for evaluating LLM prompts before deploying them to production.
• Promptify: A library aimed at assisting in developing pipelines for using LLM APIs in production for various NLP tasks.
• PromptFlow: An open-source, low-code tool from Microsoft that allows users to create executable flowcharts integrating LLMs, prompts, Python functions, and conditional logic.
• Prompt generator for ChatGPT: Desktop tools designed to help users generate specific types of prompts, for example, character-specific prompts for ChatGPT.
• Helicone: A platform that can be used for monitoring and observability in LLM applications, which is helpful in refining prompts based on performance.
• Streamlit: While not exclusively a prompt engineering tool, it's often used for rapidly prototyping and building UIs for LLM applications, which aids in testing prompts.
• Label Studio: Used for collecting human annotations, which can be crucial for evaluating prompt outputs and creating datasets for fine-tuning or prompt optimization.

The field of prompt engineering is rapidly evolving, with new techniques and tools continually emerging as researchers and developers explore more effective ways to interact with and control large language models.

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Okay, let's delve deeper into the world of prompt engineering, exploring more advanced techniques, nuanced aspects of existing ones, additional tools, the critical process of evaluation, and some future-facing considerations.

Expanding on Prompt Engineering Techniques

While the foundational techniques are crucial, the field is constantly evolving with more sophisticated methods:

• ReAct (Reasoning and Acting): This technique combines reasoning and acting within the LLM. The model generates verbal reasoning traces (similar to CoT) to deduce a plan and then "acts" by interacting with external tools (like a search engine or a calculator) to gather information or perform computations. The observations from these actions are then used to refine the reasoning and subsequent actions. This allows LLMs to tackle more complex, multi-step problems that require external knowledge or tools.
• Decomposed Prompting: For highly complex tasks, this approach breaks the problem down into smaller, manageable sub-problems. Each sub-problem is then addressed by a dedicated prompt or a series of prompts. The solutions to these sub-problems are then synthesized to form the final answer. This is particularly useful when a single prompt might be too long or too complex for the LLM to handle effectively.
• Self-Critique and Refinement: This involves prompting the LLM to first generate a response and then critique its own response based on certain criteria (e.g., factual accuracy, completeness, clarity, adherence to constraints). Following the critique, the LLM can then be prompted to refine its initial response, leading to higher quality outputs. This can be an iterative process.
• APE (Automatic Prompt Engineer): This is a more meta-level technique where LLMs themselves are used to automatically generate and select effective prompts for other tasks. It often involves providing the LLM with examples of good input-output pairs for a target task and then prompting it to generate candidate instructions. These generated instructions are then evaluated, and the best-performing ones are selected.
• Multimodal Prompting: With the rise of multimodal LLMs (like Gemini), prompting is no longer limited to text. Prompts can now include images, audio, and even video inputs, and the model can be asked to perform tasks that require understanding and integrating information from these different modalities. For example, "Describe the scene in this image and suggest a suitable caption" or "What is the sentiment expressed in this audio clip?"
• Program-Aided Language Models (PALs) and Tool-Augmented LLMs: Similar to ReAct, these techniques involve augmenting LLMs with the ability to write and execute code (e.g., Python). The LLM can offload parts of a problem, especially those requiring precise calculations or structured data manipulation, to a code interpreter. The prompt guides the LLM to generate the appropriate code.
• Adding Constraints and Negative Prompts: While it's generally better to instruct what to do, sometimes specifying what not to do (negative constraints) can be helpful, especially for refining style or avoiding common pitfalls. However, this should be used judiciously as LLMs can sometimes misinterpret or over-focus on the negative. Explicitly stating constraints on length, format, topics to avoid, or sources to ignore can also be very effective.
• Iterative Refinement of Examples in Few-Shot Prompts: The quality and relevance of examples in few-shot prompting are paramount. Iteratively refining these examples based on model performance can significantly improve results. This might involve changing the wording, the structure, or even the specific content of the examples.

Additional Tools and Platforms

The landscape of prompt engineering tools continues to grow:

• Vellum: A platform for developing, testing, and deploying production-ready LLM applications. It offers features for prompt templating, A/B testing prompts, versioning, and monitoring.
• Humanloop: Provides tools for evaluating and improving LLM applications. It helps collect feedback on model responses, fine-tune models, and experiment with different prompts. It emphasizes a data-driven approach to prompt optimization.
• Weights & Biases (W&B): While a broader MLOps platform, W&B is increasingly used for tracking experiments with LLMs, including prompt variations, model configurations, and evaluation metrics. Its logging and visualization capabilities are valuable for systematic prompt engineering.
• FlowiseAI & Langflow: These are open-source UI tools for visually building LLM applications using LangChain. They offer drag-and-drop interfaces to chain together LLMs, prompts, and tools, making it easier to prototype and experiment with complex prompt sequences.
• Specific IDE Extensions: Look for extensions within popular IDEs like VS Code that are designed to streamline prompt development, testing, and management directly within the coding environment.
• Model Provider Platforms: Platforms like Google's Vertex AI (with its Generative AI Studio) and Amazon Bedrock offer robust environments for experimenting with prompts for their respective foundation models, including tools for evaluation and iteration.

The Crucial Role of Prompt Evaluation

Designing prompts is only half the battle; evaluating their effectiveness is equally, if not more, important.

• Defining Clear Metrics: Before testing prompts, define what success looks like. Metrics can include:
  • Accuracy: For tasks with objective right/wrong answers (e.g., classification, question answering).
  • Relevance: How well the output addresses the prompt.
  • Coherence & Fluency: The quality of the language used.
  • Completeness: Whether the response provides all necessary information.
  • Helpfulness: How useful the response is to the end-user.
  • Adherence to Constraints: Did the model follow instructions regarding length, format, tone, etc.?
  • Absence of Bias/Harm: Ensuring the output is fair, unbiased, and not harmful.
  • Efficiency/Latency: How quickly the response is generated (though more a model than a prompt metric, complex prompts can increase latency).
• Creating Evaluation Sets: Develop a diverse set of input examples (an "eval set") that represent the range of scenarios your LLM application will encounter. This set should include typical cases as well as edge cases.
• Human Evaluation: For many tasks, especially those involving subjectivity (e.g., creative writing, summarization quality), human evaluation is indispensable. Raters can assess outputs based on predefined rubrics.
• Model-Based Evaluation: Using another LLM (a "judge" LLM) to evaluate the output of the primary LLM is an emerging technique. This can be faster and cheaper than human evaluation but requires careful prompt design for the judge LLM itself.
• A/B Testing: When you have multiple candidate prompts, A/B testing them with real users or on your evaluation set can help determine which performs better according to your chosen metrics.
• Regression Testing: Once you have a good prompt, save it along with its evaluation set. If you make changes to the prompt or the underlying model, re-run the evaluation to ensure performance hasn't degraded (a "regression").

The Iterative Cycle of Prompt Engineering

Effective prompt engineering is rarely a one-shot process. It's an iterative cycle:

1. Design & Implement: Craft an initial prompt based on best practices and the specific task.
2. Test: Run the prompt with a variety of inputs (ideally from your eval set).
3. Analyze Results: Examine the outputs, identify errors, inconsistencies, or areas for improvement against your defined metrics.
4. Refine: Modify the prompt (e.g., change wording, add examples, adjust structure, incorporate new techniques) based on the analysis.
5. Retest: Repeat the process until the desired level of performance is achieved.

Future Directions and Advanced Considerations

• Personalization: Prompts that can dynamically adapt to individual user preferences, history, or context.
• Adaptive Prompting: Systems where the LLM itself can request clarification or suggest improvements to the prompt if it's ambiguous or incomplete.
• Cross-Lingual Prompting: Techniques for effectively prompting LLMs in one language to perform tasks or generate content in another.
• Ethical AI and Responsible Prompting: Increased focus on developing prompts that inherently promote fairness, reduce bias, prevent misuse, and ensure safety. This includes techniques to make models more robust against adversarial prompting (jailbreaking).
• Explainability of Prompt Influence: Research into better understanding why certain prompts work well and how different parts of a prompt influence the LLM's output.

By continuously exploring these advanced techniques, leveraging the right tools, rigorously evaluating outputs, and embracing an iterative mindset, you can significantly enhance your ability to harness the power of large language models.

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You're looking to delve even deeper into prompt engineering! This field is indeed rapidly advancing, and moving beyond a basic understanding of techniques and tools to a more strategic and nuanced approach is key to unlocking the full potential of Large Language Models (LLMs).

Here’s a more in-depth exploration, focusing on practical application, advanced workflows, managing prompt ecosystems, the broader skillset involved, and what the near future holds as of May 2025.

1. The Evolving Role of the Prompt Engineer: Beyond Crafting

The role of a prompt engineer is increasingly becoming that of an AI interaction strategist and designer. It's not just about writing good questions; it's about:

• Understanding Model Psychology: Developing an intuition for how different models "think," their biases, strengths, and weaknesses.
• Designing Conversational Flows: For complex tasks, designing a sequence of prompts and responses, anticipating user needs and potential model failures.
• Optimizing for Cost and Latency: Crafting prompts that are not only effective but also efficient in terms of computational resources and response time.
• Ensuring Ethical and Responsible Outputs: Actively working to design prompts that minimize harmful biases, misinformation, and other undesirable outcomes.
• Cross-functional Collaboration: Working closely with developers, domain experts, UX designers, and product managers to integrate LLMs into applications effectively.

2. Advanced Practical Workflows & Iterative Refinement

Effective prompt engineering is a highly iterative and empirical process. A typical advanced workflow looks like this:

1. Define Clear Objectives & Metrics:
   • What is the exact desired output? (e.g., code generation, summarization, data extraction, creative writing).
   • How will success be measured? (e.g., accuracy, completeness, adherence to format, user satisfaction, reduction in task completion time).
2. Initial Prompt Design (Hypothesis):
   • Start with a clear, concise instruction.
   • Incorporate foundational techniques: persona, zero-shot/few-shot, CoT if applicable.
   • Specify output format, tone, and constraints.
   • Consider using structured prompts (e.g., using XML tags or Markdown to delineate sections like context, instructions, examples, and output format).
3. Systematic Testing & Evaluation:
   • Develop an "Eval Set": Create a diverse set of input examples that cover typical use cases, edge cases, and potential failure points.
   • Manual and Automated Testing:
     • Manual Review: For nuanced quality aspects (e.g., creativity, coherence).
     • Automated Metrics: For quantifiable aspects (e.g., keyword presence, code compilability, format correctness).
     • A/B Testing: Comparing different prompt versions on the same inputs.
   • Model-Based Evaluation: Using another LLM (a "judge" LLM) to assess the quality of the primary LLM's output, though this also requires careful prompt design for the judge.
4. Analysis & Debugging (see section 3):
   • Identify patterns in failures. Is the prompt too vague? Is the context insufficient? Is the model hallucinating?
5. Iterative Refinement:
   • Incremental Changes: Modify one aspect of the prompt at a time to isolate what works.
   • Experiment with Advanced Techniques: Introduce or combine techniques like ReAct, self-critique, or more complex few-shot example sets.
   • Refine Examples: In few-shot prompts, the quality and diversity of examples are critical. Iterate on these as much as on the instructions.
   • Adjust Parameters: Experiment with model parameters like temperature and top_p (if available) in conjunction with prompt changes.
6. Version Control & Documentation: Keep track of prompt versions, the rationale for changes, and their performance on eval sets.

3. Prompt Debugging and Overcoming Common Challenges

When prompts don't yield desired results, systematic troubleshooting is essential:

• Ambiguity and Vagueness:
  • Problem: Model produces generic, off-target, or unfocused responses.
  • Solution: Increase specificity. Use precise language. Define jargon. Clearly state the desired outcome and scope. Break down complex requests.
• Model Hallucinations (Generating Untrue Information):
  • Problem: Model confidently states incorrect facts or makes things up.
  • Solution:
    • Provide grounding context or use Retrieval Augmented Generation (RAG) to feed factual information into the prompt.
    • Instruct the model to state when it doesn't know an answer or to cite sources.
    • Cross-verify critical information.
    • Reduce temperature for more factual, less creative outputs.
• Context Window Limitations / Forgetting Instructions:
  • Problem: In long interactions or with lengthy prompts, the model "forgets" earlier parts of the instruction or context.
  • Solution:
    • Place the most critical instructions at the end of the prompt.
    • Summarize long contexts or conversations periodically.
    • Break down very long tasks into smaller, chained prompts.
    • Use models with larger context windows if available.
• Inconsistent Tone or Style:
  • Problem: Model output doesn't match the desired persona or style.
  • Solution:
    • Use explicit role-playing ("Act as a...") and clearly define the desired tone (e.g., "formal," "enthusiastic," "skeptical").
    • Provide few-shot examples that embody the target style.
    • Use self-critique prompts to ask the model to review its own tone.
• Overly Complex Prompts:
  • Problem: Trying to get the model to do too many things in one prompt can confuse it.
  • Solution: Decompose the task into simpler sub-tasks, each with its own prompt. Use prompt chaining.
• Bias Amplification:
  • Problem: Model reflects or amplifies biases present in its training data.
  • Solution:
    • Explicitly instruct the model to consider diverse perspectives or avoid biased language.
    • Carefully review outputs for bias.
    • Fine-tune models with balanced datasets (a more involved solution).
    • Explore ethical prompting frameworks and checklists.
• Cross-Model Inconsistency:
  • Problem: A prompt that works well with one LLM (e.g., GPT-4o) may not work as well with another (e.g., Claude 3.5 Sonnet or Llama 3).
  • Solution: Be prepared to adapt and re-optimize prompts when changing models due to differences in architecture, training data, and inherent capabilities.

4. Managing Prompt Ecosystems: Scalability and Collaboration

For individuals, managing a few prompts is simple. For organizations building LLM-powered applications, a more systematic approach is needed:

• Centralized Prompt Repositories/Libraries:
  • Store prompts in a version-controlled system (like Git).
  • Organize prompts by task, application, or domain.
  • Include metadata: version history, author, performance metrics, intended model, and usage guidelines.
• Prompt Templating: Create reusable prompt templates with placeholders for dynamic content. This ensures consistency and makes it easier to update prompts.
• Version Control (e.g., PromptLayer, custom Git workflows):
  • Track changes to prompts over time.
  • Allow rollback to previous versions.
  • Facilitate A/B testing of different prompt versions.
• Collaboration Tools: Platforms that allow teams to share, comment on, and collaboratively refine prompts.
• Monitoring and Analytics:
  • Track prompt performance in production (e.g., user satisfaction, error rates, desired outcomes achieved).
  • Identify underperforming prompts that need refinement.
  • Tools like Helicone or PromptLayer can assist here.
• Documentation: Thoroughly document each prompt, explaining its purpose, how it works, the rationale behind its design, and examples of good inputs/outputs.

5. Synergy of Prompt Engineering, RAG, and Fine-Tuning

These are not mutually exclusive but rather complementary approaches:

• Prompt Engineering: The first and often most cost-effective step. Maximizes the capabilities of a pre-trained model through clever input design. Always the starting point.
• Retrieval Augmented Generation (RAG): Use when tasks require access to specific, up-to-date, or proprietary external knowledge not present in the model's training data. Prompts are designed to effectively utilize the retrieved information. RAG helps combat hallucinations and provides current context.
• Fine-Tuning: More resource-intensive. Involves further training a pre-trained model on a smaller, domain-specific dataset.
  • When to consider fine-tuning:
    • To adapt the model to a very specific style, tone, or format that is hard to achieve consistently with prompting alone.
    • To teach the model highly specialized knowledge or tasks not well-covered by RAG.
    • When you need to significantly alter the model's behavior or embed very specific expertise.
    • When you have a substantial amount of high-quality labeled data for the specific task.
  • Note: Even with a fine-tuned model, effective prompt engineering is still crucial to elicit the best performance. Parameter-Efficient Fine-Tuning (PEFT) methods make this more accessible.

Typical decision flow: Start with prompt engineering. If performance is insufficient due to lack of knowledge, integrate RAG. If specialized behavior or style adaptation is deeply needed and prompting/RAG aren't enough (and you have data), consider fine-tuning. Prompt engineering is then applied to the fine-tuned model.

6. Domain-Specific Prompt Engineering Nuances

While general principles apply, strategies can vary:

• Software Development: Prompts for code generation, explanation, debugging, or test case creation. Often involves providing code snippets, specifying languages, libraries, and desired functionality. Techniques like ReAct (generating code, testing it, then refining) are powerful.
• Scientific Research: Prompts for literature review summaries, hypothesis generation, data analysis suggestions, or drafting manuscript sections. Requires precise language, referencing specific concepts, and potentially integrating with data analysis tools.
• Education: Prompts for creating lesson plans, generating quiz questions, providing personalized explanations, or acting as a Socratic tutor. Needs to adapt to different learning levels and pedagogical goals.
• Creative Industries (Writing, Marketing, Design): Prompts for brainstorming ideas, generating marketing copy, writing scripts, or creating visual concepts (for multimodal models). Often involves more open-ended prompts, emphasis on tone, style, and originality. Iteration and "mega-prompts" (very long, detailed prompts) are common.

7. The "Meta" Skills of Prompt Engineering

Beyond specific techniques, successful prompt engineers cultivate:

• Analytical Thinking & Problem Decomposition: Breaking down complex problems into steps amenable to LLM processing.
• Creativity & Lateral Thinking: Exploring unconventional ways to ask questions or frame problems.
• Attention to Detail: Small changes in wording, punctuation, or structure can significantly impact output.
• Empathy (for the model and the user): Understanding how the model might "interpret" a prompt and how the end-user will perceive the output.
• Persistence & Experimental Mindset: Prompt engineering is often trial and error. A willingness to experiment and learn from failures is key.
• Strong Language Skills: A deep understanding of nuance, ambiguity, and clarity in language.

8. Future Outlook & Continuous Learning (as of May 2025)

The field is incredibly dynamic. Key trends to watch and adapt to:

• Automated Prompt Optimization/Generation: Tools (like Google's Promptbreeder or DSPy) and techniques (like APE) that use AI to help discover or refine optimal prompts are becoming more sophisticated. This doesn't replace human engineers but augments their capabilities.
• Multimodal Prompting is Mainstream: Effectively prompting models that understand and generate text, images, audio, and video simultaneously is crucial.
• Adaptive and Context-Aware Prompting: LLMs are getting better at understanding underspecified prompts or asking clarifying questions. Prompts might become more like dialogues or high-level goal descriptions.
• Enhanced Focus on Ethical & Responsible AI: Robust techniques and tools for creating prompts that mitigate bias, ensure fairness, and prevent misuse are paramount. Safety guardrails and moderation APIs are integral.
• Rise of AI Agents and Complex Workflows: Prompting is key to orchestrating chains of LLM calls and interactions with external tools (as seen in LangChain-like frameworks and agentic systems).
• Personalization: Prompts tailored to individual user history, preferences, and context will lead to more relevant and effective AI assistance.
• No-Code/Low-Code Prompting Platforms: Making sophisticated prompt engineering accessible to non-technical users through intuitive interfaces.
• Industry-Specific LLMs and Prompting Needs: Models pre-trained or fine-tuned for specific domains (e.g., healthcare, finance, law) will require specialized prompting expertise.

Prompt engineering is less about a fixed set of rules and more about developing a flexible, adaptive skillset to effectively communicate intent to increasingly powerful AI systems. Continuous learning, experimentation, and engagement with the research community are vital to staying at the forefront.

 Examples

 

Okay, here are 100 example prompts across various categories, showcasing different techniques and purposes.

I. Creative Writing & Content Generation

1. Zero-Shot Story: "Write a short story about a lonely lighthouse keeper who discovers a mysterious, glowing object washed ashore."
2. With Constraints: "Compose a 100-word flash fiction piece in the horror genre, set in a seemingly ordinary supermarket."
3. Poetry (Specific Form): "Write a haiku about the feeling of the first snowfall."
4. Marketing Copy: "Generate 5 catchy taglines for a new eco-friendly coffee brand."
5. Blog Post Idea: "Brainstorm 3 blog post titles and a brief outline for each, targeting beginner gardeners."
6. Script Snippet (Dialogue): "Write a short dialogue (5-7 lines) between two detectives at a bizarre crime scene."
7. Product Description: "Craft a compelling product description (around 150 words) for a smart water bottle that tracks hydration and glows to remind you to drink."
8. Song Lyrics (Verse): "Write the first verse and chorus for a folk song about a journey to a mythical land."
9. Role-Play Content: "You are a travel blogger. Write an enthusiastic Instagram caption for a photo of the Eiffel Tower at sunset."
10. Few-Shot (Style Mimic): "Here's a style: 'The wind whispered secrets through the skeletal trees. Shadows danced like phantoms in the pale moonlight.' Now, write a description of an old, abandoned library in a similar style."

II. Information Retrieval & Summarization

11. Explain Concept (Simple): "Explain the concept of photosynthesis in simple terms for a 5th grader."
12. Explain Concept (Advanced): "Provide a detailed explanation of quantum entanglement, including its potential applications."
13. Summarize Article (Provide Text): "Summarize the following article [insert article text here] into three key bullet points."
14. Q&A (Factual): "What were the main causes of World War I?"
15. Comparison: "Compare and contrast the economic systems of capitalism and socialism, highlighting 2 pros and 2 cons for each."
16. Define Term: "Define 'cognitive dissonance' and provide an everyday example."
17. List Generation: "List 10 major inventions of the 21st century so far."
18. How-To (Simple): "How do I tie a Windsor knot?"
19. Pros and Cons: "What are the pros and cons of remote work for employees?"
20. Historical Context: "Describe the social and political climate in the United States during the 1960s."

III. Problem Solving & Reasoning

21. Simple Math (CoT): "What is 37 multiplied by 12? Show your step-by-step reasoning."
22. Logic Puzzle: "A man is looking at a portrait. Someone asks him whose portrait he is looking at. He replies, 'Brothers and sisters I have none, but that man's father is my father's son.' Whose portrait is the man looking at? Explain your reasoning."
23. Planning (Travel): "Create a 3-day itinerary for a first-time visitor to Rome, focusing on historical sites and local cuisine."
24. Decision Making (Framed): "I want to learn a new skill. Should I learn Python or Spanish? Consider factors like job prospects, difficulty, and personal enrichment. Provide a balanced recommendation."
25. Hypothetical Scenario: "Imagine a world where gravity suddenly became half as strong. What are three immediate consequences for everyday life?"
26. Brainstorming Solutions: "My houseplants keep dying. What are 5 potential reasons and solutions?"
27. Debugging (Conceptual): "If a website's images are not loading for users, what are the first three things a developer should check?"
28. Ethical Dilemma: "A self-driving car encounters a situation where it must either hit a pedestrian or swerve and risk injuring its passenger. Discuss the ethical considerations."
29. Strategic Thinking: "What are three potential strategies a small bookstore could use to compete against large online retailers?"
30. Chain of Thought (Complex): "A farmer has 17 sheep. All but 9 die. How many live sheep does he have left? Explain the steps to arrive at the answer."

IV. Coding & Technical Tasks

31. Generate Python Function: "Write a Python function that takes a list of numbers and returns the sum of all even numbers in the list."
32. Explain Code Snippet: "Explain what this JavaScript code does: const greet = (name) => console.log(\Hello, ${name}!`);`"
33. SQL Query: "Write an SQL query to find all customers from the 'Customers' table who live in 'California' and have made a purchase in the last 30 days from the 'Orders' table."
34. HTML Structure: "Generate the basic HTML boilerplate structure for a webpage."
35. Regex Generation: "Create a regular expression to validate an email address."
36. Debugging (Specific Error): "I'm getting a 'TypeError: 'NoneType' object is not iterable' in Python. What does this error typically mean and how can I debug it?"
37. Code Commenting: "Add concise comments to the following Python function explaining its purpose and parameters: def calc_area(l, w): return l * w"
38. Translate Code (Conceptual): "Explain how you would approach converting a simple 'for loop' from Python to JavaScript."
39. API Endpoint Idea: "Suggest 3 API endpoints for a weather application."
40. Technical Documentation Snippet: "Write a brief explanation for a README file on how to install a hypothetical Python package using pip."

V. Learning & Education

41. Lesson Plan Idea: "Outline a lesson plan for a 45-minute class on the water cycle for 4th graders."
42. Generate Quiz Questions: "Create 5 multiple-choice questions about the planets in our solar system."
43. Explanation for Student: "Explain the Pythagorean theorem to a student who is struggling with algebra, using a simple analogy."
44. Study Tips: "Provide 5 effective study tips for preparing for a history exam."
45. Flashcard Content: "Generate 3 flashcard prompts (front and back) for learning Spanish vocabulary related to food."
46. Discussion Prompt: "Create a thought-provoking discussion prompt for a high school literature class after reading 'To Kill a Mockingbird'."
47. Concept Map Ideas: "Suggest key concepts and connections for a concept map about 'Climate Change'."
48. Role-Play (Tutor): "Act as a friendly math tutor. A student says, 'I don't understand fractions.' How do you begin to help them?"
49. Simplify Complex Text: "Rewrite the following scientific abstract [insert abstract] in a way that a high school student can understand."
50. Career Advice (for Student): "What advice would you give a high school student interested in a career in artificial intelligence?"

VI. Business & Professional

51. Draft Email: "Draft a polite follow-up email to a potential client who hasn't responded to a proposal sent last week."
52. Meeting Agenda: "Create a meeting agenda for a 1-hour project kick-off meeting."
53. Brainstorm Business Idea: "Generate 3 unique business ideas that combine technology and sustainability."
54. SWOT Analysis (Framework): "Provide a framework for conducting a SWOT analysis for a small coffee shop." (You'd then fill it in)
55. Presentation Outline: "Outline a 10-minute presentation on the 'Importance of Cybersecurity for Small Businesses'."
56. Job Description Snippet: "Write the 'Responsibilities' section for a job description for a 'Social Media Manager'."
57. Negotiation Strategy (Conceptual): "What are three key principles of effective negotiation?"
58. Customer Service Response: "A customer is angry because their order was late. Draft a calm and empathetic response."
59. Market Research Question: "What are 3 key questions to ask in a survey to understand customer satisfaction with a software product?"
60. Mission Statement Idea: "Help me brainstorm a mission statement for a non-profit organization focused on adult literacy."

VII. Role-Playing & Persona

61. Historical Figure: "Act as Leonardo da Vinci. Describe your excitement about one of your inventions."
62. Expert Persona: "You are a seasoned financial advisor. Explain the concept of 'dollar-cost averaging' to a novice investor."
63. Fictional Character: "Imagine you are Sherlock Holmes. Deduce why a teacup might be found smashed on the library floor."
64. Alien Perspective: "You are an alien visiting Earth for the first time. Describe your first impressions of a busy city street."
65. Animal Perspective: "Act as a house cat. Describe your typical day from your point of view."
66. Object Persona: "You are an old, leather-bound book in a forgotten library. Tell a brief story about one of your readers."
67. Career Counselor: "Act as a career counselor. A client is unsure whether to pursue a creative passion or a stable job. How do you advise them?"
68. Chef Persona: "You are a world-renowned chef. Describe the inspiration behind your signature dish."
69. Debate Partner: "Take the stance that renewable energy is the only viable future for global power needs and provide three supporting arguments."
70. Customer Support (Challenging): "Act as a patient customer support agent. A user is frustrated because they can't log in, but they keep typing their password incorrectly. Guide them."

VIII. Data Manipulation & Analysis (Conceptual/Instructional)

71. Data Cleaning Idea: "If I have a dataset of customer addresses with many typos, what are three general steps I could take to clean it?"
72. Chart Suggestion: "What type of chart would be best to visualize the change in sales figures for 5 different products over the past year?"
73. Interpreting Trend (Hypothetical): "If a graph shows a steady increase in website traffic but a decrease in conversion rates, what could be two potential interpretations?"
74. Key Metrics: "What are 3 key metrics a SaaS company should track to measure customer engagement?"
75. Data Structuring: "How should I structure a simple JSON object to store information about a book (title, author, publication year, genres)?"
76. A/B Test Explanation: "Explain the concept of A/B testing in the context of website design."
77. SQL Join Explanation: "In simple terms, when would I use an INNER JOIN versus a LEFT JOIN in SQL?"
78. Data Anonymization Idea: "What are two common techniques for anonymizing personal data in a dataset before sharing it?"
79. Pattern Identification (Conceptual): "If I'm looking at user activity logs, what kinds of patterns might indicate a security breach?"
80. Report Section Outline: "Outline the key sections for a data analysis report that presents findings on customer churn."

IX. Fun & MiscellaneousOkay, here are 100 example prompts across various categories, showcasing different techniques and purposes.

**I. Creative Writing & Content Generation**

1.  **Zero-Shot Story:** "Write a short story about a lonely lighthouse keeper who discovers a mysterious, glowing object washed ashore."
2.  **With Constraints:** "Compose a 100-word flash fiction piece in the horror genre, set in a seemingly ordinary supermarket."
3.  **Poetry (Specific Form):** "Write a haiku about the feeling of the first snowfall."
4.  **Marketing Copy:** "Generate 5 catchy taglines for a new eco-friendly coffee brand."
5.  **Blog Post Idea:** "Brainstorm 3 blog post titles and a brief outline for each, targeting beginner gardeners."
6.  **Script Snippet (Dialogue):** "Write a short dialogue (5-7 lines) between two detectives at a bizarre crime scene."
7.  **Product Description:** "Craft a compelling product description (around 150 words) for a smart water bottle that tracks hydration and glows to remind you to drink."
8.  **Song Lyrics (Verse):** "Write the first verse and chorus for a folk song about a journey to a mythical land."
9.  **Role-Play Content:** "You are a travel blogger. Write an enthusiastic Instagram caption for a photo of the Eiffel Tower at sunset."
10. **Few-Shot (Style Mimic):**
    "Here's a style: 'The wind whispered secrets through the skeletal trees. Shadows danced like phantoms in the pale moonlight.'
    Now, write a description of an old, abandoned library in a similar style."

**II. Information Retrieval & Summarization**

11. **Explain Concept (Simple):** "Explain the concept of photosynthesis in simple terms for a 5th grader."
12. **Explain Concept (Advanced):** "Provide a detailed explanation of quantum entanglement, including its potential applications."
13. **Summarize Article (Provide Text):** "Summarize the following article [insert article text here] into three key bullet points."
14. **Q&A (Factual):** "What were the main causes of World War I?"
15. **Comparison:** "Compare and contrast the economic systems of capitalism and socialism, highlighting 2 pros and 2 cons for each."
16. **Define Term:** "Define 'cognitive dissonance' and provide an everyday example."
17. **List Generation:** "List 10 major inventions of the 21st century so far."
18. **How-To (Simple):** "How do I tie a Windsor knot?"
19. **Pros and Cons:** "What are the pros and cons of remote work for employees?"
20. **Historical Context:** "Describe the social and political climate in the United States during the 1960s."

**III. Problem Solving & Reasoning**

21. **Simple Math (CoT):** "What is 37 multiplied by 12? Show your step-by-step reasoning."
22. **Logic Puzzle:** "A man is looking at a portrait. Someone asks him whose portrait he is looking at. He replies, 'Brothers and sisters I have none, but that man's father is my father's son.' Whose portrait is the man looking at? Explain your reasoning."
23. **Planning (Travel):** "Create a 3-day itinerary for a first-time visitor to Rome, focusing on historical sites and local cuisine."
24. **Decision Making (Framed):** "I want to learn a new skill. Should I learn Python or Spanish? Consider factors like job prospects, difficulty, and personal enrichment. Provide a balanced recommendation."
25. **Hypothetical Scenario:** "Imagine a world where gravity suddenly became half as strong. What are three immediate consequences for everyday life?"
26. **Brainstorming Solutions:** "My houseplants keep dying. What are 5 potential reasons and solutions?"
27. **Debugging (Conceptual):** "If a website's images are not loading for users, what are the first three things a developer should check?"
28. **Ethical Dilemma:** "A self-driving car encounters a situation where it must either hit a pedestrian or swerve and risk injuring its passenger. Discuss the ethical considerations."
29. **Strategic Thinking:** "What are three potential strategies a small bookstore could use to compete against large online retailers?"
30. **Chain of Thought (Complex):** "A farmer has 17 sheep. All but 9 die. How many live sheep does he have left? Explain the steps to arrive at the answer."

**IV. Coding & Technical Tasks**

31. **Generate Python Function:** "Write a Python function that takes a list of numbers and returns the sum of all even numbers in the list."
32. **Explain Code Snippet:** "Explain what this JavaScript code does: `const greet = (name) => console.log(\`Hello, ${name}!\`);`"
33. **SQL Query:** "Write an SQL query to find all customers from the 'Customers' table who live in 'California' and have made a purchase in the last 30 days from the 'Orders' table."
34. **HTML Structure:** "Generate the basic HTML boilerplate structure for a webpage."
35. **Regex Generation:** "Create a regular expression to validate an email address."
36. **Debugging (Specific Error):** "I'm getting a 'TypeError: 'NoneType' object is not iterable' in Python. What does this error typically mean and how can I debug it?"
37. **Code Commenting:** "Add concise comments to the following Python function explaining its purpose and parameters: `def calc_area(l, w): return l * w`"
38. **Translate Code (Conceptual):** "Explain how you would approach converting a simple 'for loop' from Python to JavaScript."
39. **API Endpoint Idea:** "Suggest 3 API endpoints for a weather application."
40. **Technical Documentation Snippet:** "Write a brief explanation for a README file on how to install a hypothetical Python package using pip."

**V. Learning & Education**

41. **Lesson Plan Idea:** "Outline a lesson plan for a 45-minute class on the water cycle for 4th graders."
42. **Generate Quiz Questions:** "Create 5 multiple-choice questions about the planets in our solar system."
43. **Explanation for Student:** "Explain the Pythagorean theorem to a student who is struggling with algebra, using a simple analogy."
44. **Study Tips:** "Provide 5 effective study tips for preparing for a history exam."
45. **Flashcard Content:** "Generate 3 flashcard prompts (front and back) for learning Spanish vocabulary related to food."
46. **Discussion Prompt:** "Create a thought-provoking discussion prompt for a high school literature class after reading 'To Kill a Mockingbird'."
47. **Concept Map Ideas:** "Suggest key concepts and connections for a concept map about 'Climate Change'."
48. **Role-Play (Tutor):** "Act as a friendly math tutor. A student says, 'I don't understand fractions.' How do you begin to help them?"
49. **Simplify Complex Text:** "Rewrite the following scientific abstract [insert abstract] in a way that a high school student can understand."
50. **Career Advice (for Student):** "What advice would you give a high school student interested in a career in artificial intelligence?"

**VI. Business & Professional**

51. **Draft Email:** "Draft a polite follow-up email to a potential client who hasn't responded to a proposal sent last week."
52. **Meeting Agenda:** "Create a meeting agenda for a 1-hour project kick-off meeting."
53. **Brainstorm Business Idea:** "Generate 3 unique business ideas that combine technology and sustainability."
54. **SWOT Analysis (Framework):** "Provide a framework for conducting a SWOT analysis for a small coffee shop." (You'd then fill it in)
55. **Presentation Outline:** "Outline a 10-minute presentation on the 'Importance of Cybersecurity for Small Businesses'."
56. **Job Description Snippet:** "Write the 'Responsibilities' section for a job description for a 'Social Media Manager'."
57. **Negotiation Strategy (Conceptual):** "What are three key principles of effective negotiation?"
58. **Customer Service Response:** "A customer is angry because their order was late. Draft a calm and empathetic response."
59. **Market Research Question:** "What are 3 key questions to ask in a survey to understand customer satisfaction with a software product?"
60. **Mission Statement Idea:** "Help me brainstorm a mission statement for a non-profit organization focused on adult literacy."

**VII. Role-Playing & Persona**

61. **Historical Figure:** "Act as Leonardo da Vinci. Describe your excitement about one of your inventions."
62. **Expert Persona:** "You are a seasoned financial advisor. Explain the concept of 'dollar-cost averaging' to a novice investor."
63. **Fictional Character:** "Imagine you are Sherlock Holmes. Deduce why a teacup might be found smashed on the library floor."
64. **Alien Perspective:** "You are an alien visiting Earth for the first time. Describe your first impressions of a busy city street."
65. **Animal Perspective:** "Act as a house cat. Describe your typical day from your point of view."
66. **Object Persona:** "You are an old, leather-bound book in a forgotten library. Tell a brief story about one of your readers."
67. **Career Counselor:** "Act as a career counselor. A client is unsure whether to pursue a creative passion or a stable job. How do you advise them?"
68. **Chef Persona:** "You are a world-renowned chef. Describe the inspiration behind your signature dish."
69. **Debate Partner:** "Take the stance that renewable energy is the only viable future for global power needs and provide three supporting arguments."
70. **Customer Support (Challenging):** "Act as a patient customer support agent. A user is frustrated because they can't log in, but they keep typing their password incorrectly. Guide them."

**VIII. Data Manipulation & Analysis (Conceptual/Instructional)**

71. **Data Cleaning Idea:** "If I have a dataset of customer addresses with many typos, what are three general steps I could take to clean it?"
72. **Chart Suggestion:** "What type of chart would be best to visualize the change in sales figures for 5 different products over the past year?"
73. **Interpreting Trend (Hypothetical):** "If a graph shows a steady increase in website traffic but a decrease in conversion rates, what could be two potential interpretations?"
74. **Key Metrics:** "What are 3 key metrics a SaaS company should track to measure customer engagement?"
75. **Data Structuring:** "How should I structure a simple JSON object to store information about a book (title, author, publication year, genres)?"
76. **A/B Test Explanation:** "Explain the concept of A/B testing in the context of website design."
77. **SQL Join Explanation:** "In simple terms, when would I use an INNER JOIN versus a LEFT JOIN in SQL?"
78. **Data Anonymization Idea:** "What are two common techniques for anonymizing personal data in a dataset before sharing it?"
79. **Pattern Identification (Conceptual):** "If I'm looking at user activity logs, what kinds of patterns might indicate a security breach?"
80. **Report Section Outline:** "Outline the key sections for a data analysis report that presents findings on customer churn."

**IX. Fun & Miscellaneous**

81. **Joke Generation:** "Tell me a pun about computers."
82. **Trivia Question:** "Create a challenging trivia question about ancient Egyptian mythology."
83. **Recipe Idea:** "Suggest a simple recipe using only chicken breast, broccoli, and rice."
84. **Workout Idea:** "Generate a 20-minute bodyweight workout routine for beginners."
85. **Book Recommendation:** "Recommend a science fiction novel for someone who enjoyed 'Dune'."
86. **Movie Plot Twist Idea:** "Brainstorm a surprising plot twist for a detective movie."
87. **Conversation Starter:** "Give me 3 interesting conversation starters for a networking event."
88. **Game Idea:** "Come up with a simple word game that can be played by two people."
89. **Random Fact:** "Tell me an interesting and little-known fact about marine biology."
90. **Quote Generation:** "Generate an inspirational quote about perseverance."

**X. Advanced/Complex Prompts (Showcasing Techniques)**

91. **Few-Shot with CoT:**
    "Q: Natalia sold clips to 48 of her friends and then found 7 more. If each clip costs $2, how much money did she make?
    A: Natalia sold clips to 48 friends. She found 7 more clips. So, she has 48 + 7 = 55 clips. Each clip costs $2. So, she made 55 * 2 = $110.
    Q: A clown had 20 balloons. He bought 20 more and then burst 10. How many balloons does he have left?
    A: The clown had 20 balloons. He bought 20 more, so he had 20 + 20 = 40 balloons. He burst 10 balloons. So he has 40 - 10 = 30 balloons left.
    Q: John has 5 apples. He buys 3 more boxes of apples, and each box contains 4 apples. How many apples does John have in total?
    A:"
92. **Self-Critique and Refine:** "Write a paragraph describing a sunset. Then, critique your own paragraph for vividness and originality. Finally, rewrite the paragraph based on your critique."
93. **ReAct (Conceptual):** "Goal: Find out the current weather in Paris and suggest an appropriate outfit.
    Thought: I need to find the current weather first. I will use a search tool.
    Action: Search("current weather in Paris")
    Observation: (Assume search returns 'Paris weather: 15°C, light rain')
    Thought: 15°C with light rain means it's cool and damp. I should suggest layers and something waterproof.
    Action: Suggest("A light waterproof jacket, a sweater, comfortable trousers, and waterproof shoes.")
    Now, using this ReAct format, plan how to answer: 'What were the key arguments in the 2024 US presidential election debates?'"
94. **Decomposed Prompting (Instruction):** "To write a comprehensive company profile, I will ask you for information in parts.
    First, describe the company's mission and vision.
    Second, detail its core products or services.
    Third, outline its target market and key competitors.
    Finally, summarize its history and key achievements.
    Let's start with: Describe the mission and vision of a hypothetical sustainable energy startup called 'SunSpark Innovations'."
95. **Constrained Generation + Persona:** "You are a grumpy, old pirate. Write a 4-line sea shanty about looking for treasure, but you must include the words 'barnacles' and 'gold'."
96. **Template Filling:** "Fill in the following template for a project update:
    Project Name: [Your Input]
    Date: [Current Date]
    Completed Tasks: [List 2-3 completed tasks]
    Ongoing Tasks: [List 2-3 ongoing tasks]
    Blockers: [Mention any blockers, or 'None']
    Next Steps: [List 1-2 next steps]
    Provide the input for a 'Website Redesign Project'."
97. **Comparative Analysis with Criteria:** "Compare two popular project management tools, Asana and Trello, based on the following criteria: ease of use for beginners, collaboration features, and pricing for small teams. Present your answer in a table format."
98. **Multi-Turn Conversation (Instruction):** "I want you to help me plan a surprise birthday party. I will ask you questions in sequence.
    My first question is: What are the first 5 things I need to consider when planning a surprise party?"
99. **Generating a Checklist:** "Create a detailed checklist for someone preparing to go on an international flight for the first time, covering documents, packing, and airport procedures."
100. **Explain with Analogy + Constraints:** "Explain the concept of 'blockchain' to a non-technical person using an analogy related to a public ledger, but avoid using the word 'crypto' or 'Bitcoin'. Keep the explanation under 200 words."

These examples should give you a good starting point for experimenting with different ways to interact with LLMs!

81. Joke Generation: "Tell me a pun about computers."
82. Trivia Question: "Create a challenging trivia question about ancient Egyptian mythology."
83. Recipe Idea: "Suggest a simple recipe using only chicken breast, broccoli, and rice."
84. Workout Idea: "Generate a 20-minute bodyweight workout routine for beginners."
85. Book Recommendation: "Recommend a science fiction novel for someone who enjoyed 'Dune'."
86. Movie Plot Twist Idea: "Brainstorm a surprising plot twist for a detective movie."
87. Conversation Starter: "Give me 3 interesting conversation starters for a networking event."
88. Game Idea: "Come up with a simple word game that can be played by two people."
89. Random Fact: "Tell me an interesting and little-known fact about marine biology."
90. Quote Generation: "Generate an inspirational quote about perseverance."

X. Advanced/Complex Prompts (Showcasing Techniques)

91. Few-Shot with CoT: "Q: Natalia sold clips to 48 of her friends and then found 7 more. If each clip costs $2, how much money did she make? A: Natalia sold clips to 48 friends. She found 7 more clips. So, she has 48 + 7 = 55 clips. Each clip costs $2. So, she made 55 * 2 = $110. Q: A clown had 20 balloons. He bought 20 more and then burst 10. How many balloons does he have left? A: The clown had 20 balloons. He bought 20 more, so he had 20 + 20 = 40 balloons. He burst 10 balloons. So he has 40 - 10 = 30 balloons left. Q: John has 5 apples. He buys 3 more boxes of apples, and each box contains 4 apples. How many apples does John have in total? A:"
92. Self-Critique and Refine: "Write a paragraph describing a sunset. Then, critique your own paragraph for vividness and originality. Finally, rewrite the paragraph based on your critique."
93. ReAct (Conceptual): "Goal: Find out the current weather in Paris and suggest an appropriate outfit. Thought: I need to find the current weather first. I will use a search tool. Action: Search("current weather in Paris") Observation: (Assume search returns 'Paris weather: 15°C, light rain') Thought: 15°C with light rain means it's cool and damp. I should suggest layers and something waterproof. Action: Suggest("A light waterproof jacket, a sweater, comfortable trousers, and waterproof shoes.") Now, using this ReAct format, plan how to answer: 'What were the key arguments in the 2024 US presidential election debates?'"
94. Decomposed Prompting (Instruction): "To write a comprehensive company profile, I will ask you for information in parts. First, describe the company's mission and vision. Second, detail its core products or services. Third, outline its target market and key competitors. Finally, summarize its history and key achievements. Let's start with: Describe the mission and vision of a hypothetical sustainable energy startup called 'SunSpark Innovations'."
95. Constrained Generation + Persona: "You are a grumpy, old pirate. Write a 4-line sea shanty about looking for treasure, but you must include the words 'barnacles' and 'gold'."
96. Template Filling: "Fill in the following template for a project update: Project Name: [Your Input] Date: [Current Date] Completed Tasks: [List 2-3 completed tasks] Ongoing Tasks: [List 2-3 ongoing tasks] Blockers: [Mention any blockers, or 'None'] Next Steps: [List 1-2 next steps] Provide the input for a 'Website Redesign Project'."
97. Comparative Analysis with Criteria: "Compare two popular project management tools, Asana and Trello, based on the following criteria: ease of use for beginners, collaboration features, and pricing for small teams. Present your answer in a table format."
98. Multi-Turn Conversation (Instruction): "I want you to help me plan a surprise birthday party. I will ask you questions in sequence. My first question is: What are the first 5 things I need to consider when planning a surprise party?"
99. Generating a Checklist: "Create a detailed checklist for someone preparing to go on an international flight for the first time, covering documents, packing, and airport procedures."
100. Explain with Analogy + Constraints: "Explain the concept of 'blockchain' to a non-technical person using an analogy related to a public ledger, but avoid using the word 'crypto' or 'Bitcoin'. Keep the explanation under 200 words."

These examples should give you a good starting point for experimenting with different ways to interact with LLMs!