AI Tools UX Research: How Do These Tools Handle Large Documents?

Imagine this: you upload an 80-page interview transcript to your AI tool, ask a question about the last 20 pages, and the AI responds with a friendly ‘There's nothing about that in the document.’ Frustrating? Absolutely. Avoidable? Yes, if you understand how AI tools for UX research really work.

In this article, you'll learn how ChatGPT, Claude, and Gemini handle large UX documents, where the hidden pitfalls lie, and how to get the most out of your analyses. As a UX consultant, I've been working with qualitative research methods for over 25 years and test AI tools extensively in my daily work. The differences I've discovered are greater than expected.

📌 The most important points in brief:

• ChatGPT automatically breaks down large documents into chunks, which can hide overarching patterns.

• Claude often processes medium-sized documents (up to approx. 150,000 words) in their entirety and only switches to chunking when there is an overflow.

• Gemini has the largest context window (up to 2 million tokens) and can process entire research archives at once

• The context window determines how much text an AI can ‘see’ at one time

• Test your tools specifically: ask for content from different parts of documents to discover gaps

• Choosing the right tool can determine the quality of your UX insights

What is a context window, and why should you care?

The context window is the ‘attention span’ of an AI. It defines how much text the system can process at once. Anything beyond that must either be ignored or processed in another way.

This is crucial for UX research: if your interview transcript is larger than the context window, the AI cannot recognise all the connections. Imagine someone reading your 100-page research document but forgetting the previous pages after each page. That's exactly what happens when AI tools only partially capture UX research documents.

As of January 2025: ChatGPT offers 128,000 tokens, Claude between 200,000 and 1 million tokens, and Gemini up to 2 million tokens. One token corresponds to approximately 0.75 words in German.

How do ChatGPT, Claude and Gemini handle large documents?

The three major AI providers have developed fundamentally different strategies to deal with the context window problem. These differences have a direct impact on the quality of your UX analyses.

ChatGPT: The chunking approach

ChatGPT relies on Retrieval Augmented Generation (RAG). The system automatically breaks down large documents into smaller sections, stores them in a search index, and only retrieves the supposedly relevant parts when questions are asked.

The problem in practice: In a 60-page usability test report, I asked about recurring patterns of frustration. ChatGPT provided examples from the first 20 pages, but overlooked a key pattern that only became apparent from comments on pages 45 and 52. The chunks were simply not ‘close enough’ to each other to be recognised as belonging together.

When ChatGPT works well: The system delivers reliable results for specific individual questions (‘What did participant 3 say about navigation?’). The search usually finds the relevant section without any problems.

Claude: The hybrid approach

Claude pursues a different strategy: as long as a document fits into the context window, it is processed completely and coherently. Only when the limit is exceeded does the system automatically switch to RAG.

The advantage: With medium to large interview transcripts (up to about 150,000 words), you have a good chance of complete processing. In my work as a UX consultant, I therefore prefer to use Claude for analyses where overarching themes and subtle patterns are important.

Good to know: Technically, it makes no difference whether you paste text or upload it as a file. Both are processed identically as long as they fit into the context window.

Gemini: The brute force approach

Google is taking a radically different approach with Gemini: instead of cleverly breaking things down, it simply built a huge context window. Up to 2 million tokens, which is equivalent to about 1,500 pages of text.

For UX research, this means that you can theoretically upload multiple interview transcripts, usability logs and background documents at the same time. Gemini keeps track of everything.

The catch: This approach is resource-intensive and correspondingly expensive. For regular analyses of large amounts of data, you should keep an eye on the costs.

AI tools UX research: A direct comparison

How can you tell that your AI tool hasn't read everything?

There are typical warning signs that indicate that your AI tool has only partially captured UX research documents:

Answers only refer to the beginning: If all examples and quotes come from the first few pages, the AI probably hasn't looked at the entire document.

Overarching patterns are missing: When asked about recurring themes, the AI only provides isolated individual examples instead of connections.

The AI seems ‘surprised’: When asked specific questions about later parts of the document, the system reacts as if it is seeing the content for the first time.

Contradictory statements: The AI makes statements that contradict other parts of the document because it does not see them at the same time.

My tip: For important analyses, always ask control questions about different parts of the document. Ask about connections between the beginning and the end. When fully processed, the AI should be able to recognise these connections.

How to get the most out of your AI tools for UX research

Tips for ChatGPT

Formulate your prompts specifically and explicitly indicate that the entire document should be taken into account. For complex analyses, it can be helpful to divide the document into thematic sections and analyse them one after the other. Alternatively, you can copy the entire text directly into the chat window, and everything will be processed.

Tips for Claude

Use the power of the large context window for analyses where overarching themes are important. For very large files, it helps to explicitly ask for different parts of the document to ensure that RAG mode finds all relevant information.

Tips for Gemini

Use its power for really large amounts of data, for example, if you want to analyse several interviews or an entire research project at the same time. For repeated requests to the same documents, context caching is worthwhile to save costs.

Which AI tool is suitable for which UX research task?

Short to medium interviews (up to 30 pages): All three systems work reliably. Choose according to personal preference or existing subscription.

Large individual interviews (50+ pages): Prefer Claude or Gemini. ChatGPT will have to chunk here, which can lead to gaps in pattern analysis.

Multiple documents at once: Gemini is strongest here. Claude can still manage it with medium overall sizes. ChatGPT quickly reaches its limits.

Specific individual questions: ChatGPT works excellently here, as the chunk search finds exactly the relevant section.

Code reviews and prototype analyses: Gemini excels with its ability to understand up to 30,000 lines of code at once.

Frequently asked questions about AI tools for UX research

Does it make a difference whether I copy text or upload it as a file?

Not with Claude; both methods are processed identically. With ChatGPT, pasting directly into the chat window can be advantageous because then no chunking takes place. With Gemini, it doesn't matter either.

Can I see if my document has been fully processed?

Unfortunately, the systems do not display this directly. You can test it by asking specific questions about different parts of the document. If the AI consistently recognises connections between the beginning and the end, the document has probably been captured in its entirety.

Which tool is best for confidential UX research data?

All three providers have enterprise options with advanced data protection features. Check the respective data protection guidelines and clarify with your company which platforms are approved. The technical differences in document processing remain the same in the enterprise versions.

Will AI tools for UX research be better at handling large documents in the future?

The trend is clearly towards larger context windows. Claude is currently expanding to 1 million tokens, and Gemini is experimenting with even larger windows. This does not make RAG obsolete, but it does make it less necessary. For UX teams, this means less technical understanding is required and more focus can be placed on intelligent questioning.

Conclusion: Know your tool, optimise your results

The three major AI tools for UX research have fundamentally different approaches to handling large documents. ChatGPT relies on chunking and is particularly suitable for specific individual questions. Claude uses a hybrid approach and often processes medium-sized documents in their entirety. Gemini offers the largest context window and is suitable for very large amounts of data.

Choosing the right tool can determine the quality of your UX insights. If important patterns disappear in the ‘chunking gap,’ you may be making incomplete recommendations.

My recommendation: Test your typical UX documents with different systems. Ask identical questions and compare the quality of the answers. Investing in the right tool will quickly pay off.

Want to learn more about effective AI workflows for UX research? Let's talk. I'll show you how to take your research processes to the next level with the right tools and techniques.

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