Why PM prompts fail
Most PM prompts ask open-ended questions: "What should our pricing strategy be?" AI responds with textbook answers. The fix: constrain the problem space, demand options, and require explicit rationale.
Pattern 1: Options Generator
Purpose: Get three distinct approaches instead of one generic recommendation.
Bad prompt:
"What's the best way to prioritize our product backlog?"
Better prompt:
"Generate three distinct prioritization frameworks for our B2B SaaS backlog. For each:
- Name the framework and its core principle
- List 2 scenarios where it excels
- List 2 scenarios where it fails
- Estimate effort to implement (hours)
Constraints: Team of 5 engineers, quarterly planning cycle, enterprise customers."
Why it works: Forces comparison, surfaces trade-offs, includes context AI needs.
Pattern 2: Constraint Stress-Test
Purpose: Expose hidden assumptions and edge cases.
Bad prompt:
"Review this feature spec for issues."
Better prompt:
"You are a skeptical stakeholder reviewing this feature spec. Identify:
- Three assumptions that could be wrong
- Two edge cases not addressed
- One regulatory or compliance risk
- The weakest part of the business case
Spec: [paste spec]
Format: Bullet each issue with severity (high/medium/low) and suggested mitigation."
Why it works: Role assignment + structured output + severity rating = actionable feedback.
Pattern 3: Rationale Extractor
Purpose: Get the "why" behind recommendations, not just the "what."
Bad prompt:
"Should we build or buy this capability?"
Better prompt:
"Compare build vs. buy for [capability] using this framework:
| Factor | Build | Buy | Weight (1-5) | |--------|-------|-----|--------------| | Time to market | | | | | Total cost (3 yr) | | | | | Strategic fit | | | | | Risk profile | | | | | Team capability | | | |
For each cell, provide a 1-sentence rationale. Conclude with weighted recommendation and confidence level (high/medium/low)."
Why it works: Forces structured reasoning, makes trade-offs visible, includes confidence.
- ✓Prompt includes specific constraints (team size, timeline, customer type)
- ✓Prompt requests multiple options, not single recommendation
- ✓Prompt demands rationale for each claim
- ✓Output format specified (table, bullets, sections)
- ✓Prompt includes "what could go wrong" or edge case request
Quick Reference: Bad → Better
| Bad | Better | |-----|--------| | "What should we do?" | "Compare three approaches given [constraints]" | | "Review this for issues" | "Identify assumptions, edge cases, and risks with severity" | | "Is this a good idea?" | "Score against [criteria] with rationale for each" | | "Write a PRD" | "Draft PRD sections with explicit success metrics and anti-goals" |
“"We went from 4 prompt iterations to 1 when we started including constraints and demanding options. The output was usable on first pass."”
Related Resources
- PM Quickstart — full workflow for PM packs
- Bias Spotting Checklist — validate AI recommendations
Apply this now
Practice prompt
Take your most-used PM prompt and rewrite it using the Options-Constraints-Rationale pattern.
Try this now
Run the PM Mini-Pack with your rewritten prompt and compare output quality.
Common pitfall
Asking open-ended questions—AI will give you textbook answers instead of context-specific options.
Key takeaways
- •Include constraints in every prompt—team size, timeline, customer type
- •Demand three options with trade-offs instead of one recommendation
- •Require rationale and confidence levels for actionable output
See it in action
Drop this into a measured run—demo it, then tie it back to your methodology.
See also
Pair this play with related resources, methodology notes, or quickstarts.
Further reading
Next Steps
Ready to measure your AI impact? Start with a quick demo to see your Overestimation Δ and cognitive load metrics.