Prompt engineering is the methodical practice of designing inputs that elicit useful, targeted responses from language models. Based on OpenAI’s Prompting Guide for GPT-4, here are five specific techniques product managers can apply to use ChatGPT more effectively.

1. Be Specific: Provide Scope and Structure

Ineffective prompt:
"Summarize customer feedback."

Improved version:
"Summarize the top three recurring problems mentioned in this feedback. For each, include the user persona, relevant product area, and one representative quote."

Rationale: Specific prompts reduce ambiguity and help the model focus on actionable insights.

2. Provide Examples for Calibration

If you're looking for structured responses, show ChatGPT what good looks like.

Prompt:
"Format feedback as shown:

• Input: 'App crashes during login'
• Output:
• • Persona: IT Admin
  • Theme: Stability
  • Sentiment: Negative
  • Quote: 'App crashes during login.'

Now, do the same for the following feedback set."

Rationale: Examples work as informal training, guiding the model toward your preferred structure and style.

3. Ask for Structured Output

Instead of generating long-form text, request formatted output that's ready to use in documentation.

Prompt:

For each theme identified in the feedback, format the output as follows:

## Problem Theme: <Theme>
- Personas: <List>
- Quotes:
- "<Quote 1>"
- "<Quote 2>"
- Frequency: <X out of Y instances>

Rationale: Structured responses are easier to scan, compare, and incorporate into product docs and planning materials.

4. Define ChatGPT's Role

Assigning a role to ChatGPT aligns its responses with your expectations.

Prompt:
"You are a product analyst reviewing customer feedback from school districts. Identify user pain points, hypothesize potential root causes, and propose relevant product areas."

Rationale: Role-based prompts influence tone, analytical depth, and response format.

5. Treat Prompts Like Product Iterations

Prompt engineering benefits from the same mindset as feature development: iteration and testing.

Approach:

• Begin with a draft prompt
• Test on a small dataset
• Adjust: Add constraints, clarify language, change structure

Rationale: Continuous refinement helps surface better insights, reduce hallucinations, and tailor output to your workflow.

Summary

Just like software engineering, prompt engineering helps you get the most out of a system. Learning to engineer it well could become a key differentiator in your PM Craft.