"Batch" in Gemini refers to processing multiple items (files, data rows, or prompts) simultaneously rather than one by one.
Here is how batch processing works depending on how you are using Gemini:
1. Batch File Analysis (For Everyday Users)
You can "batch" analyze documents by uploading multiple files into a single chat window. Gemini's large context window allows it to "read" all of them at once.
The Use Case: Comparing documents or searching across a library of files.
How to do it:
Click the
+icon and upload multiple files (e.g., 5 PDF contracts, 3 CSVs, or a mix of formats).Prompt: "Compare the liability clauses across these 5 contracts" or "Based on these 3 spreadsheets, which region had the highest growth?"
Constraint: You are limited by the context window (currently 1-2 million tokens depending on your tier), which is roughly equivalent to 700,000+ words or hundreds of documents.
2. Batch Processing in Google Sheets (For Workspace Users)
If you have a subscription to Gemini for Google Workspace, you can perform batch operations directly inside spreadsheets. This is the most powerful "no-code" batch tool.
The Feature: Using Gemini to classify, extract, or generate text for hundreds of rows at once.
How to do it:
Smart Fill / Help Me Organize: You can ask Gemini to "Classify these customer reviews as Positive, Neutral, or Negative" for an entire column.
Custom Functions: You can use a prompt in one cell and drag it down to apply that prompt to every row in your dataset.
3. The Batch API (For Developers)
If you are building an app or running a script, Google offers a Batch API for Gemini.
What it is: A way to send thousands of prompts to Gemini at once. These are processed asynchronously (not in real-time) and are returned when complete.
Why use it:
Cost: It is significantly cheaper (often 50% off) compared to standard synchronous calls.
Volume: It avoids rate limits (hitting the "too many requests" error) when processing huge datasets.
Summary: How do you want to use Batch?
| If you want to... | Use this... |
| Compare 10 PDFs | Gemini Chat: Upload all files at once. |
| Label 500 spreadsheet rows | Gemini in Sheets: Use workspace features. |
| Process 10,000 prompts via code | Batch API: Use the developer platform. |
Do you have a specific set of data or files you are trying to process right now?
"Iteration in research" with Gemini refers to using AI not just for a single search, but as a multi-step engine that refines, fact-checks, and expands on information.
This happens in two ways: Autonomous Iteration (where Gemini does the loop for you via the Deep Research feature) and Collaborative Iteration (where you guide the process manually).
1. Autonomous: The "Deep Research" Agent
Gemini has a specialized mode (often called Deep Research) designed specifically for iterative investigation. Instead of running a single Google search, it acts as an agent that loops through a research cycle automatically.
How it iterates:
Planning: It breaks your complex question (e.g., "Compare the supply chain risks of EV batteries in 2025 vs 2030") into smaller sub-tasks.
Execution: It searches for information on the first sub-task.
Gap Analysis (The Iteration): It analyzes the findings. If information is missing or contradictory, it autonomously formulates new queries to fill those gaps.
Synthesis: It combines all iterations into a comprehensive report with citations.
Why this matters: A standard AI response might give you a surface-level summary. Deep Research iterates until it has enough depth to answer the full scope of your prompt.
2. Collaborative: The Manual Iterative Workflow
If you are using the standard Gemini chat or need precise control, you act as the "director" of the iteration. You don't ask for the final result immediately; you build it in stages.
The "Research & Refine" Loop:
Phase 1: Broad Scan:
Prompt: "Give me a high-level overview of [Topic]."
Phase 2: Drill Down (Iterate on Specifics):
Prompt: "You mentioned [Factor X] in the previous answer. Expand on that specifically, focusing on recent developments."
Phase 3: Challenge & Verify:
Prompt: "Are there any counter-arguments to this theory?" or "Find sources that disagree with this conclusion."
Phase 4: Synthesis:
Prompt: "Based on our conversation, write a final summary."
3. Internal Iteration: "Deep Thinking"
For research involving logic, math, or data analysis (rather than web searching), Gemini uses a process called Deep Thinking.
What it is: Before answering, the model "thinks" silently, testing different hypotheses and correcting its own logic errors.
The Iteration: It might generate a solution, realize it's flawed, and iterate on the logic before showing you the final answer.
Summary: Which one do you need?
| Feature | Best For... | Type of Iteration |
| Deep Research | Market reports, literature reviews, complex topics. | Autonomous: The AI loops through searches for you. |
| Standard Chat | Brainstorming, quick answers, exploring ideas. | Manual: You guide the AI through follow-up prompts. |
| Deep Thinking | Math, coding, logic puzzles. | Internal: The AI iterates on its own logic before answering. |
Would you like to try a Deep Research task? You can give me a complex topic (like "Investigate the current state of solid-state battery production"), and I can help you plan the research steps.
"Reference images" in Gemini work in two distinct ways: using them to guide the creation of new content (style/structure reference) or using them to analyze/discuss the image itself.
Here is how to use reference images for both purposes:
1. Using Reference Images for Generation (Style & Structure)
If you want to generate a new image or video that looks like an image you already have, you can use a reference image to transfer the style or composition.
Style Transfer: You can upload an image and ask Gemini to generate something new in that specific style.
How to do it: Upload a photo (e.g., a sketch, an oil painting, or a specific aesthetic) and prompt: "Generate an image of a futuristic city in the style of this image."
Structure/Composition: You can use a simple sketch or photo to dictate where objects should be.
How to do it: Upload a rough sketch and prompt: "Turn this sketch into a photorealistic image of a mountain landscape."
Video Generation (Veo): You can use an image to define the starting point or style of a generated video.
How to do it: Upload an image and prompt: "Generate a video of this character blinking and smiling."
2. Using Reference Images for Analysis
You can also upload images simply to give Gemini context or to ask questions about them.
Visual Q&A: Ask questions about what is in the photo.
Example: "What breed of dog is this?" or "Explain the diagram in this picture."
Problem Solving: Upload a picture of a math problem or a broken appliance.
Example: "Solve this equation" or "What is this part of my dishwasher called?"
Creative Writing: Ask Gemini to write a story or caption based on the image.
Example: "Write a melancholic poem about this landscape."
How to Add a Reference Image
Locate the Plus Icon: In the chat box (usually on the left side), click the
+icon.Upload: Select "Upload image" and choose your file.
Prompt: Once the thumbnail appears in your chat bar, type your instruction (e.g., "Make a new image in this style" or "Describe this image") and hit send.
Would you like to try generating an image in a specific style, or do you need help analyzing a photo you have?
"Iteration" in the context of Gemini usually refers to one of three things: the process of refining your prompts to get better results, using Gemini to write repetitive code (loops), or the specific tools available to refine images.
Here is a breakdown of how iteration works in these different contexts.
1. Iterative Prompting (Refining Results)
This is the most common form of iteration. Rarely is the first prompt perfect. You work with Gemini in a "loop" to refine the output until it matches your vision.
The Follow-up: instead of restating your whole request, treat it like a conversation.
First Prompt: "Write an email to my boss about a delay."
Iteration: "Make it more professional," or "Mention that the delay is due to a vendor issue."
The "Modify" Controls: At the bottom of a text response, you will often see buttons to instantly iterate on the tone or length without typing a new prompt:
Shorter / Longer
More Casual / More Professional
2. Iteration in Coding (Loops)
If you are using Gemini to help you write code, "iteration" refers to control flow statements (loops) that repeat a block of code. You can ask Gemini to generate these for you.
Example Prompt: "Write a Python script that iterates through a list of files in a directory and renames them."
Gemini's Output: It will generate
forloops orwhileloops relevant to the language you are using.
3. Iterating on Images
Gemini (specifically the Nano Banana model) allows for conversational iteration on images. You don't have to generate a brand new image from scratch if the first one is almost right.
Step 1: Generate an image. ("Generate an image of a futuristic car.")
Step 2: Refine it with natural language. ("Make the car red," or "Put it on the moon.")
Step 3: Gemini modifies the existing image concept rather than starting purely from zero, maintaining the context of your previous iterations.
Which type of iteration were you looking for?
If you clarify what you are trying to achieve (e.g., "I want to improve a story" or "I need to fix a coding loop"), I can give you a more specific example!
You are absolutely right! The "Nano Banana" model is the engine behind my image generation tools, and one of its standout features is exactly what you mentioned: Iterative Refinement.
This means you don't have to get the perfect prompt on the very first try. We can work together to tweak and evolve an image until it matches your vision.
How to Iterate with Me
Generate a Base Image:
Start with a standard prompt (e.g., "Generate an image of a futuristic city made of crystal").
Give Follow-up Instructions:
Once the image is created, you can simply talk to me to change it. You don't need to rewrite the whole prompt.
Examples:
"Make it look like it's sunset."
"Add a flying car in the foreground."
"Change the style to watercolor."
Refine Details:
The model maintains the context of our conversation, so it understands that "Make it brighter" refers to the image we just created.
Other "Nano Banana" Capabilities
Image Editing: You can upload an image and ask me to change specific elements.
Style Transfer: You can provide reference images to guide the style of the new generation.
Text Rendering: It is highly capable of rendering legible text within images (e.g., signs, labels, logos).
Would you like to test it out? Give me a starting prompt, and we can iterate on it together!
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"Looping" in Gemini usually refers to one of three concepts: Agentic Looping (the AI correcting itself), Programming Loops (writing repetitive code), or Creative Looping (seamless video/audio).
Here is the breakdown of how looping functions in these different areas.
1. Agentic Looping (Self-Correction)
This is the most advanced form of "looping" available in Gemini's logic. It refers to the model's ability to perform an action, observe the result, and loop back to try again if it failed—without you having to ask.
The "Code Execution" Loop: When you ask Gemini to solve a complex math problem or process data, it writes and runs Python code in the background.
The Loop: If the code returns an error, Gemini reads the error message, "loops" back to rewrite the code, and runs it again. It may do this several times in seconds until it gets the right answer.
The "ReAct" Pattern (Reason + Act): Gemini uses a loop of Thought $\rightarrow$ Action $\rightarrow$ Observation. It thinks about what to do, uses a tool (like Search), observes the data, and then loops back to think about the next step.
Key Difference: "Iteration" (your previous question) is usually user-guided (you tell it to change). "Looping" is often system-guided (the AI fixes itself automatically).
2. Looping in Code Generation
If you are asking Gemini to write software, "looping" refers to generating standard control flow structures that repeat tasks.
Standard Loops: You can ask for
forloops (repeat a specific number of times) orwhileloops (repeat until a condition changes).Vectorization (Advanced): If you ask for a loop in Python (e.g., for data analysis), Gemini might suggest avoiding a standard loop and using "vectorization" (processing all data at once) for better performance. It is smart enough to optimize your loops.
3. Creative Looping (Video & Animation)
With Gemini's video generation capabilities (Veo), "looping" refers to creating content that plays seamlessly on repeat.
Seamless Video Loops: You can prompt Gemini to generate background assets meant to loop.
Prompt: "Generate a cinematic video of a fireplace with crackling fire, designed to loop seamlessly."
GIF Creation: You can ask Gemini to create short, repetitive animations suitable for GIFs.
Summary: Which loop do you need?
| Type | Context | What happens |
| Agentic Loop | Data/Math | Gemini tries a solution $\rightarrow$ Fails $\rightarrow$ Retries automatically. |
| Code Loop | Programming | Gemini writes code (for/while) to repeat a task. |
| Media Loop | Creative | Gemini generates a video that repeats seamlessly. |
Are you trying to automate a task (Agentic), write a script (Code), or make a background video (Creative)?
"Agentic" describes a shift in how Gemini operates: instead of just being a chatbot that talks about things, it becomes an agent that does things.
An "Agentic" AI doesn't just predict the next word in a sentence; it formulates a plan, uses tools to execute that plan, and adapts if things go wrong.
Here is what Agentic behavior looks like in Gemini:
1. The Core Agentic Loop: Reason, Act, Observe
When you give Gemini a complex command (like "Find the cheapest flight to Tokyo and put it in a spreadsheet"), it doesn't just guess. It enters an agentic loop:
Reason: It breaks the request down. "First I need to search for flights. Then I need to extract the price. Then I need to open a spreadsheet."
Act: It uses a tool. It calls Google Flights or Search.
Observe: It looks at the results.
Refine: If the search failed (e.g., no flights found), it doesn't give up. It reasons again: "Maybe I should change the date slightly" and tries again.
2. Agentic Capabilities in Gemini
Currently, Gemini exhibits agentic behavior in three specific ways:
A. Code Execution Agent (The "Analyst")
This is one of the most powerful agentic features. If you upload a messy spreadsheet and say "Fix the errors and make a graph," Gemini doesn't just write text.
It writes Python code to load your file.
It runs that code in a secure sandbox.
It checks if the code worked.
If the code fails (e.g., a syntax error), it self-corrects and runs it again.
Finally, it presents the graph.
B. Deep Research Agent (The "Investigator")
As mentioned previously, this is an agent designed for information gathering.
It doesn't just do one Google search.
It explores a topic, reads multiple sources, identifies conflicting information, and synthesizes a final report. It acts like a research assistant who goes away for an hour and comes back with a dossier.
C. Workspace Agent (The "Personal Assistant")
With Gemini Extensions, the model acts as an agent across your Google apps.
Command: "Find the email from the venue, get the address, and add it to my travel itinerary doc."
Agentic Action: It chains three tools together: Gmail Search $\rightarrow$ Information Extraction $\rightarrow$ Google Docs Write.
Summary: Passive vs. Agentic
| Feature | Passive AI (Chatbot) | Agentic AI (Gemini) |
| Goal | Provide an answer. | Complete a task. |
| Method | Retrieves knowledge from training data. | Uses tools (Search, Code, Apps) to get real-time results. |
| Failure | Hallucinates or says "I don't know." | Retries, debugs, or searches again (Self-correction). |
| Complexity | Single-turn (Question $\rightarrow$ Answer). | Multi-turn (Goal $\rightarrow$ Plan $\rightarrow$ Steps $\rightarrow$ Result). |
Would you like to see an agentic workflow in action?
Compare West Virginia schools math performance over the last ten years
You can ask me to solve a logic puzzle that requires code, or ask a question that requires connecting data from multiple searches (e.g., "Compare the stock performance of Apple and Microsoft over the last 5 years and tell me who won").
Over the last ten years, West Virginia's math performance has followed a distinct "V-shaped" trajectory: a period of stability or slight struggle pre-2020, a sharp collapse during the COVID-19 pandemic (2020–2021), and a steady, multi-year recovery that has recently brought performance back to pre-pandemic levels.
By 2025, state data indicates that schools have nearly closed the gap created by the pandemic, though West Virginia continues to trail the national average on federal assessments.
1. The "V-Shaped" Recovery (State Assessment Data)
The most clear metric for local performance is the West Virginia General Summative Assessment (WVGSA).
Pre-Pandemic (2015–2019): Math proficiency was generally stable, hovering around 39–40% of students meeting standards.
The Crash (2020–2021): Due to school closures and remote learning, math proficiency plummeted to 28% in 2021—an 11-point drop.1
The Recovery (2022–2025): Since the return to in-person learning, scores have risen every single year. As of the 2025 assessment results, proficiency hit 40.8%, effectively matching the pre-COVID high of 2019.
| Year | Math Proficiency Rate (Grades 3-8) | Trend |
| 2018-19 | ~39% | Pre-Pandemic Baseline |
| 2019-20 | No Testing | COVID-19 Outbreak |
| 2020-21 | 28% | Historical Low (The Crash) |
| 2021-22 | 33% | Recovery begins (+5%) |
| 2022-23 | 35% | Continued growth (+2%) |
| 2023-24 | 38% | Continued growth (+3%) |
| 2024-25 | 40.8% | Recovery Complete (Matches 2019) |
2. National Comparison (NAEP Scores)
While state tests show recovery, the National Assessment of Educational Progress (NAEP)—often called "The Nation's Report Card"—offers a harsher view when comparing West Virginia to the rest of the U.S.
West Virginia consistently scores below the national average, but recent data shows the state is recovering faster than many others, particularly in elementary math.
Grade 4 Math (Bright Spot): In 2024, WV 4th graders scored 232, a significant jump from 226 in 2022.2 While this is still below the national average (237), the rate of growth was among the top 10 in the country.
Grade 8 Math (Struggling): 8th-grade scores have remained stagnant. The average score in 2024 was 261, virtually unchanged from 2022 (260) and significantly lower than the national average of 272.3
Key Insight: The gap between WV and the nation is narrowing for younger students but widening or stalling for middle schoolers.
3. What drove the trends?
The Third Grade Success Act (2023): State legislation poured resources (including aides and science-of-reading/math training) into early elementary classrooms.4 This likely explains why Grade 4 scores are recovering much faster than Grade 8.
Chronic Absenteeism: A major headwind has been attendance.5 In 2022, nearly 29% of students were chronically absent.6 By 2025, this dropped to roughly 22.8%, which correlates with the recovery in math scores.
The "COVID Slide": Math took a harder hit than Reading during the pandemic because math is more sequential—missing one concept (like fractions in 4th grade) makes future concepts (like algebra in 8th grade) nearly impossible to grasp without remediation.
Summary: If you look at the last ten years as a whole, the state has spent the first half stagnating and the second half recovering from a crisis. As of late 2025, West Virginia is statistically "back to normal" relative to its own history, but still has significant work to do to catch up to the national average.
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