Unlock Better AI Outputs with Evals: The Missing Step for PMs Using AI

PMs everywhere are using AI to write PRDs, user stories, meeting summaries, and customer messaging. But there's a problem: the results often feel off, generic, or incomplete. The missing step? Evals. This post explains what evals are, why they matter for PMs using AI tools, and how to start using them effectively today.

What Are AI Evals, and Why Should PMs Care?

AI evals (evaluations) are systematic methods to assess AI-generated outputs. For product managers, they're the crucial difference between getting mediocre AI content and shipping quality work.

The core pain point for PMs using AI is simple: you get an output, but how do you know if it's good enough to ship? Is that PRD comprehensive? Does that user story cover the edge cases? Is the messaging on-brand?

Key Insight: "Vibe-checking" your AI outputs isn't enough. You need structure, repeatability, and specific criteria to truly evaluate if AI-generated content works for your product needs.

Lenny Rachitsky's excellent article on moving beyond vibe checks with AI content highlights a critical truth: our intuitive assessments often miss important details and lack consistency. Without a systematic approach, you're just hoping your gut feeling is right.

The Three Types of Evals You Can Use

There are three primary types of AI evaluations that PMs should have in their toolkit:

1. Grounded Evals

Compare the AI output against a known correct answer or source of truth. These are perfect for factual accuracy or when you have specific knowledge that must be reflected in the output.

Example: When generating technical specifications, check if all required API endpoints are mentioned and properly described against your API documentation.

2. Criteria-based Evals

Evaluate the output against a predefined rubric or set of criteria. These are versatile and can be applied to almost any PM use case.

Example: Does this user story follow the INVEST framework (Independent, Negotiable, Valuable, Estimable, Small, Testable)? Does the PRD include all required sections?

3. Preference Evals

Compare multiple outputs and select the best one based on specific criteria. Perfect for subjective content like messaging, value propositions, or UI copy.

Example: Generate three different value proposition statements, then select the one that best aligns with your brand voice and target audience.

When to Use Each Type:

  • Grounded Evals: When factual accuracy is critical (technical specs, data analysis)
  • Criteria-based Evals: For process documents with clear requirements (PRDs, user stories)
  • Preference Evals: For creative or persuasive content (marketing copy, feature descriptions)

Applying Evals to Common PM Workflows

Let's look at how to apply evals to the most common PM workflows:

PRDs

Eval Checklist

  • ✓ Are all required sections present and complete?
  • ✓ Are the goals clearly stated and measurable?
  • ✓ Is the user problem well-defined with supporting evidence?
  • ✓ Are non-goals and scope limitations explicitly stated?
  • ✓ Are there clear success metrics tied to business outcomes?

User Stories

Eval Checklist

  • ✓ Does it follow the INVEST framework?
  • ✓ Are edge cases and error states covered?
  • ✓ Are acceptance criteria present and testable?
  • ✓ Is the user benefit clear and compelling?
  • ✓ Is it aligned with the larger feature goals?

Communications and Strategy Docs

Eval Checklist

  • ✓ Is the tone appropriate for the audience?
  • ✓ Are next steps and responsibilities clearly defined?
  • ✓ Is the content concise and free of unnecessary jargon?
  • ✓ Does it align with company messaging and values?
  • ✓ Are complex concepts broken down effectively?

Launch Plans and Roadmaps

Eval Checklist

  • ✓ Are dependencies and milestones logically sequenced?
  • ✓ Are timelines realistic based on team capacity?
  • ✓ Are cross-team dependencies explicitly called out?
  • ✓ Are risks and contingency plans included?
  • ✓ Is there a clear connection to strategic objectives?

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How to Implement Evals in Your Workflow

Implementing evals doesn't have to be complicated. Here's a simple workflow:

1

Generate

Create your AI output using a clear, specific prompt

2

Evaluate

Check against your criteria or checklist

3

Improve

Refine the output based on eval feedback

4

Ship

Deliver the validated content with confidence

One of the simplest ways to implement evals is to use a checklist or structured prompt to evaluate your AI-generated content.

Sample LLM-Based Eval Prompt:

You are an expert PRD evaluator with 15+ years of product experience. Evaluate the following PRD excerpt against these criteria:

1. Clarity of problem statement
2. Specificity of success metrics
3. Completeness of requirements
4. Consideration of edge cases
5. Overall quality and actionability

Rate each criterion from 1-5 and provide specific feedback for improvement.

PRD excerpt: [paste your AI-generated PRD here]

This evaluation workflow fits seamlessly into standard PM processes:

  • Sprint Planning: Evaluate user stories before adding to sprint
  • Doc Reviews: Use evals as pre-review checks before sharing with stakeholders
  • Async Workflows: Share both the content and its eval score for transparent quality

Tools and Frameworks You Can Use

You don't need fancy tools to get started with evals. Here are some practical options:

PMPrompt.com

Generate content and evaluate it in one place with our specialized PM templates.

Explore Templates →

Custom Checklists

Create your own evaluation checklists in Notion or Google Docs for different document types.

Get Checklist Template →

OpenAI + Eval Frameworks

Use OpenAI's evals library or prompt engineering techniques to create robust evaluations.

OpenAI Evals →

For those looking to dive deeper, there are excellent resources like DeepLearning.ai's course on evaluation frameworks and Maven's workshop on implementing AI quality protocols.

The Future of PM + Evals

Where is this all heading? The future of product management will increasingly involve AI agents doing work, with PMs focused on reviewing, tweaking, and ensuring quality. Here's what's coming:

  • Team-level evals and benchmarks

    Teams will establish quality baselines and track improvements over time (e.g., team PRD quality scores tracked quarter over quarter).

  • Automated quality gates

    AI systems that automatically check documents and suggest improvements before they reach human reviewers.

  • Trust and explainability as differentiators

    Products that can explain their AI-generated content decisions will win in enterprise environments where quality assurance is paramount.

Conclusion

AI won't replace product managers, but PMs who know how to evaluate AI will replace those who don't. By implementing systematic evals, you'll transform AI from an interesting novelty into a reliable productivity multiplier.

Start building your eval muscle today — it's the key to making AI useful, not just novel. Your stakeholders might not see the evaluation process, but they'll definitely notice the difference in quality.

Next Steps

  • Choose one document type and create an eval checklist for it
  • Try running an AI-generated document through your eval process
  • Experiment with different types of evals for different PM workflows