Module 2 of 4Lesson 4 of 5~15 min
Four techniques that change the result
When a well-written prompt still returns an unstable format or made-up content, a few targeted techniques often make the difference. This lesson helps you recognize the problem you are facing and pick the technique that addresses it, so the summaries and documents you share are more reliable.
Lesson objective
By the end of this lesson, you will be able to add to a prompt the technique that fits the problem at hand (examples, data delimiters, breaking the task into steps, permission to be uncertain) and justify your choice.
Topics covered
- few-shot prompting
- prompting techniques
- reducing hallucinations
- task decomposition
- prompt delimiters
Where it fits
Delegate, describe, verify
How do you get a reliable result from generative AI, and know when not to trust it?
Lessons in this module
- Choosing what to hand over to AI
- Understanding what an LLM does when you prompt it
- Describing the task in a structured prompt
- Four techniques that change the result (this lesson)
- Checking and iterating with discernment
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