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Module
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.

Take this lesson: your first course is freeSee the full syllabus

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

In the glossary

Full glossary

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

  1. Choosing what to hand over to AI
  2. Understanding what an LLM does when you prompt it
  3. Describing the task in a structured prompt
  4. Four techniques that change the result (this lesson)
  5. Checking and iterating with discernment

What you will learn in this module

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.

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