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:

• How to catch the two types of AI quote hallucinations

• Why AI defaults to useless generic themes and insights

• Which LLM is best for analysis work (and which one fabricates the most)

• How to turn vague signal into actual decision clarity

• The final verification pass that stress-tests everything before it hits a deck

Referenced:

• Caitlin Sullivan: https://www.linkedin.com/in/caitlindsullivan/

• Claude Code for Customer Insights (Maven course): https://maven.com/caitlin/claude-code-insights

• Claude: https://www.anthropic.com/claude

• ChatGPT: https://chatgpt.com/

• Gemini: https://gemini.google.com/

• NotebookLM: https://notebooklm.google.com/

• Maze: https://maze.co/

• Whoop: https://www.whoop.com/

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About

Welcome to Lenny's Reads, where every week you’ll find a fresh audio version of my newsletter about building product, driving growth, and accelerating your career, read to you by the soothing voice of Lennybot.

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Listen: How to do AI analysis you can actually trust

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