What is an AI hallucination in UX research? How do I prevent AI from making up patterns?

AI hallucinations occur when AI-generated research summaries "make up patterns that aren't there" or claim insights that aren't actually supported by the source data. This is a huge professional risk that can lead to incorrect design decisions. To prevent this, perform a "Grounding Audit" where you verify that every AI-generated insight is backed by a specific timestamp in a transcript or a specific user quote. Use tools like NotebookLM that provide source-grounding, linking every claim back to the original source. Never trust AI summaries blindly—always trace insights back to specific user quotes or observations.

What is a "Grounding Audit" and how do I perform one?

A "Grounding Audit" is a quality control process where you verify every AI-generated claim against the original source material. The process: 1) Review AI-generated insights and identify all claims, 2) For each claim, find the specific timestamp or quote in the original transcript that supports it, 3) If a claim cannot be traced to a source, mark it as "unverified" or remove it, 4) Document the source for each verified claim. Use tools like NotebookLM that automatically link AI summaries to source timestamps, making the audit process faster and more reliable.

What tools help with source-grounding in AI research?

Tools that help with source-grounding include: NotebookLM (automatically links AI summaries to source timestamps), Claude with file uploads (can reference specific parts of transcripts), and custom workflows using timestamped transcripts. The key is using tools that maintain a connection between AI-generated insights and their source material, rather than tools that generate standalone summaries without source links. Always prefer tools that show you "where this came from" rather than tools that just generate insights without attribution.

AI Hallucination Audit for UX: Ensuring Data Integrity in AI-Generated Research

The Insight

A user in Screenshot 2 mentioned that AI-generated research summaries sometimes "make up patterns that aren't there." This is a huge professional risk. This Field Note teaches designers how to perform a "Grounding Audit"—a quality control process that ensures every AI-generated insight is backed by a specific timestamp in a transcript or a specific user quote. This positions you as a "Responsible AI" practitioner, which is a major hiring trend for 2026.

The Professional Risk of AI Hallucinations

AI hallucinations in UX research can lead to:

•Incorrect design decisions: Basing designs on patterns that don't actually exist in user data
•Loss of credibility: Stakeholders lose trust when insights can't be verified
•Wasted resources: Building features based on false insights wastes time and money
•Legal/ethical issues: Making claims about user behavior without evidence can be problematic

The Grounding Audit Process

A "Grounding Audit" verifies that every AI-generated claim can be traced back to its source:

AI Claim	Human Verification Step
"Users expressed frustration with checkout process"	Find specific quotes mentioning frustration + timestamp (e.g., "Interview 2, 12:34: 'I got so frustrated trying to checkout'")
"75% of users prefer mobile over desktop"	Count actual mentions in transcripts, verify percentage calculation, cite specific user quotes
"Users want a dark mode feature"	Search transcripts for "dark mode" mentions, verify if it's a direct request or inferred need
"Navigation is the primary pain point"	Review all navigation-related comments, rank by frequency, verify if it's truly "primary" or just one of many issues

Tools for Source-Grounding

NotebookLM

NotebookLM automatically links AI summaries to source timestamps, making the audit process faster and more reliable. When you ask NotebookLM to summarize an interview, it shows you exactly which parts of the transcript support each claim.

Claude with File Uploads

When you upload transcripts to Claude, you can ask it to reference specific parts of the file. Use prompts like "What did users say about the checkout process? Please cite the specific quotes and timestamps."

Custom Timestamped Workflows

Create your own workflow using timestamped transcripts. When AI generates insights, require it to include timestamps. Then verify each timestamp actually contains the claimed insight.

The Grounding Audit Checklist

Before Sharing AI-Generated Insights:

✓Trace every claim: Each insight must link to a specific timestamp or quote
✓Verify percentages: If AI says "75% of users," count the actual mentions
✓Check for inference vs. direct quotes: Distinguish between what users actually said vs. what AI inferred
✓Remove unverified claims: If a claim can't be traced to a source, remove it or mark as "needs verification"
✓Document sources: Include timestamps and quotes in your research reports

Why This Matters: Responsible AI Practice

Performing grounding audits positions you as a "Responsible AI" practitioner—a major hiring trend for 2026. Companies want designers who can:

•Use AI effectively: Leverage AI speed while maintaining quality
•Maintain data integrity: Ensure insights are accurate and verifiable
•Build stakeholder trust: Provide research that can be verified and defended

Related: Learn more about AI-Powered Research Synthesis and ensuring quality in AI-generated work.