Writing and challenging user stories
User stories written fast but full of gaps get expensive once they reach development, in questions, back-and-forth and rework. This lesson shows how to have AI produce a first draft of stories and acceptance criteria, then challenge it, so you walk into refinement with a backlog already cleared of blind spots.
Lesson objective
By the end of this lesson, you will be able to have user stories and acceptance criteria drafted from a validated opportunity, then have them critiqued (INVEST criteria, edge cases, open questions) before sharing them with the team.
Topics covered
- user stories
- acceptance criteria
- INVEST criteria
- backlog refinement
- edge cases
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
- Building a persona grounded in verbatims
- Preparing and critiquing an interview guide
- Writing and challenging user stories (this lesson)
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