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Ever run an AI analysis on customer data, only to discover the numbers were fabricated and the insights completely generic? In this episode, Caitlin Sullivan, a user-research veteran who’s trained hundreds of product and research professionals, shares her four prompting techniques for getting trustworthy, actionable insights out of any LLM. After 2,000+ hours of testing customer discovery workflows with AI, she’s identified the failure modes that break AI analysis and the reliable fixes for each one.
In this episode, you’ll learn:
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How to catch the two types of AI quote hallucinations
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Why AI defaults to useless generic themes and insights
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Which LLM is best for analysis work (and which one fabricates the most)
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How to turn vague signal into actual decision clarity
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The final verification pass that stress-tests everything before it hits a deck
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