Prompt engineering
Prompt engineering is the practice of writing and refining the instructions given to a large language model so that it produces the output you need, reliably. A prompt usually combines a task, the context the model lacks, constraints on format and tone, and sometimes examples of good answers. Inside a product, the prompt is part of the specification: it changes behavior as much as code does.
Why it matters for a PM
PMs write prompts in two settings: for their own work (synthesizing feedback, drafting specs, preparing interviews) and inside the product (system prompts for assistants and AI features). In both cases the skill resembles writing a good brief for a colleague: say what success looks like, provide the necessary material, and check the result instead of trusting it.
Example
A PM asks an assistant to “summarize this user research”. The result is bland. The second version states the audience (the design team), the goal (pick two problems for next quarter), the format (a table of problem, evidence, frequency) and asks the model to flag any claim without a supporting quote.
Key points
- Clarity beats tricks: state the goal, the audience, the expected format and the fallback when facts are lacking.
- Examples (few-shot prompting) teach format and tone faster than long descriptions.
- Delimiters such as tags or headings separate instructions from the material to process.
- A complex task works better as a sequence of steps or prompts than as one long request.
- Production prompts are versioned and evaluated on a test set before each change.
Common mistakes
- Editing a production prompt after one good try, without rerunning the cases that used to work.
- Writing vague requests (“make it better”) and blaming the model for generic output.
- Piling up instructions until they contradict each other.
- Pasting confidential data into a tool whose terms allow training on it.
Go further with Module
The courses and lessons that cover this concept: