Module 3 of 4Lesson 1 of 4~15 min
Turning customer feedback into opportunities
Having AI summarize dozens of customer comments saves hours, as long as you can trust every theme it pulls out. You learn to go from a raw pile of feedback to opportunities you can trace back to verbatims, and to know what such a synthesis cannot tell you before you bring it to a roadmap review.
Lesson objective
By the end of this lesson, you will be able to have a body of user feedback grouped into themes and opportunities, with each theme tied to verbatims you have checked, and explain what the synthesis does not tell you.
Topics covered
- customer feedback synthesis
- feedback analysis
- product opportunities
- user verbatims
- product discovery
Where it fits
Prompting on your PM deliverables
How do you apply these methods to discovery and delivery without losing fidelity to the data?
Lessons in this module
- Turning customer feedback into opportunities (this lesson)
- Building a persona grounded in verbatims
- Preparing and critiquing an interview guide
- Writing and challenging user stories
What you will learn in the course
This lesson is part of the course Prompting for Product Managers: delegate, describe, verify
- Decide, for a task in your product cycle, whether to hand it to AI, in which mode (automation, augmentation, agent), and what you keep.
- Explain how an LLM behaves (prediction, dated knowledge, working memory, instruction following) to anticipate its mistakes and pick the right fix.
- Write a prompt that describes the deliverable, the process and the quality bar, with context, delimited data and examples when needed.
- Evaluate an AI output (content, process, behavior) against explicit criteria, then improve it through targeted iterations.
- Produce PM deliverables with AI (feedback synthesis, persona, interview guide, user stories) from real data, and check that they are faithful to it.
- Configure standing instructions and a dedicated assistant for a recurring task, test it on real cases, and decide when to move to an agent.
- Apply diligence rules (shared data, transparency, accountability) before using or circulating work produced with AI.
Related courses
- Build reliable Claude Skills for your product workAdvanced · ~2 hr 30 min
- Choosing and using AI tools at workAll levels · ~1 hr 40 min
- Understand what LLMs do well, and where they failAll levels · ~2 hr