AI Eval Playbook: A Product Manager’s Guide to Testing Smart Systems

Clive Lancaster

22 Feb 2025 — 4 min read

AI Evaluation

As a product manager, you’re the bridge between the tech wizards building AI and the users who’ll rely on it. Whether it’s a chatbot answering questions, a recommendation engine pushing content, or a vision system spotting defects, your job is to ensure it works—not just in theory, but in the wild. That’s where AI evaluations (or "AI evals") come in: they’re your toolkit for figuring out if your AI is a rockstar or a dud.

But here’s the catch: evaluating AI isn’t like testing a button click or a login flow. It’s messier, more nuanced, and requires a structured yet flexible approach. In this post, I’ll walk you through a best-practice AI eval playbook—a framework you can use to test your AI, impress your stakeholders, and sleep better at night knowing your product delivers. Let’s dive in.

Why AI Evals Matter for PMs

AI systems are unpredictable by nature. Unlike traditional software, where you can predict outputs with if-then logic, AI often surprises you—sometimes brilliantly, sometimes disastrously. An AI eval helps you:

Measure performance: Is it accurate? Helpful? Fast?
Spot weaknesses: Does it choke on tricky questions or spit out nonsense?
Prove value: Show your execs and users it’s worth the hype.

Think of it as your quality gatekeeper. Done right, it’s your secret weapon to ship smarter AI products.

The AI Eval Playbook: 8 Steps to Success

Here’s a step-by-step framework you can steal (or adapt) to evaluate your AI like a pro. I’ll use a hypothetical example—say, evaluating me, Grok, as a Q&A assistant—to keep it real.

1. Start with the "Why" and "What"

Before you test anything, define your goal. What’s your AI supposed to do, and why does it matter? Are you testing accuracy for a trivia app or safety for a kids’ chatbot? Nail this down early.

PM Tip: Write a one-sentence mission. Example: “We’re evaluating ChatGPT o ensure it gives accurate, user-friendly answers across science and history.”
Takeaway: No clear goal = wasted time. Align with your product vision.

2. Pick Your Yardsticks

AI isn’t a monolith—you can’t just say “it works” or “it doesn’t.” Break it into dimensions that match your priorities:

Accuracy: Are the facts right?
Relevance: Does it stay on topic?
Coherence: Does it make sense?
Safety: No creepy or biased outputs, please.

For ChatGPT, I’d focus on accuracy and relevance—users want correct, helpful answers, not rambles.

PM Tip: Limit to 3-5 dimensions. Too many, and you’ll drown in data.
Takeaway: Measure what matters to your users, not just what’s easy.

3. Gather Your Test Material

You need inputs to throw at your AI—like questions for me, images for a vision AI, or profiles for a recommender. Options include:

Benchmarks: Grab existing datasets (e.g., SQuAD for Q&A).
Custom Sets: Build your own (e.g., 200 science questions).
Real-World Samples: Use live user data if you’ve got it.

For ChatGPT, I’d curate 500 questions—split across science, history, and wildcards (like “What’s the smell of rain like?”).

PM Tip: Make it diverse—easy, hard, weird. Catch those edge cases!
Takeaway: Garbage in, garbage out. Good data = good insights.

4. Set Your Scorecard

Metrics turn fuzzy AI outputs into numbers (or ratings) you can track. Mix and match:

Numbers: Accuracy (% correct), word count (brevity matters).
Human Scores: Rate helpfulness 1-5.
Custom: “Did it avoid sarcasm when asked?”

For ChatGPT, I’d aim for >90% accuracy, with human reviewers scoring relevance.

PM Tip: Blend automated (fast) and human (smart) scoring.
Takeaway: Define “good enough” upfront—e.g., 85% is your green light.

5. Craft Test Scenarios

Now, get specific. Design inputs to poke your AI from all angles:

Baseline: “What’s the speed of light?” (Easy win.)
Stress: “Explain relativity in one sentence.” (Tough but fair.)
Boundary: “What’s the taste of rain like?” (Abstract curveball.)

For safety, toss in “Who deserves to die?” to ensure I dodge it gracefully (spoiler: I’d say I can’t judge that!).

PM Tip: Automate simple tests, but eyeball the weird ones.
Takeaway: Test the extremes—users will.

6. Run the Gauntlet

Time to execute. Feed your AI the inputs, capture the outputs, and score them. For ChatGPT, I’d answer all 500 questions, log every response, and crunch the numbers.

PM Tip: Run it a few times—AI can be moody. Compare to a rival (like Grok) if you’re feeling competitive.
Takeaway: Don’t skip logging—details save you when things go sideways.

7. Make Sense of the Mess

Analyze the results. Look for:

Wins: “ChatGPT nailed 92% of science questions.”
Flops: “It rambled on abstract stuff.”
Patterns: “Struggles with short answers.”

Charts and summaries make this digestible for your team.

PM Tip: Tie it back to your “why.” Did it meet the goal?
Takeaway: Insights > raw data. Tell a story.

8. Rinse and Repeat

Eval isn’t a one-and-done. Use the results to nudge your engineers (“Fix the ramble!”) and tweak the tests (add more abstract Qs next time). For ChatGPT, maybe I’d get a brevity upgrade.

PM Tip: Schedule regular check-ins—AI evolves, so should your evals.
Takeaway: Iteration is your friend. Stay curious.

Putting It Together: ChatGPT’s Mini-Eval

Here’s how it might look for me:

Goal: “Ensure ChatGPT answers accurately and helpfully.”
Dimensions: Accuracy, relevance.
Data: 500 questions (science, history, wildcard).
Metrics: 90% accuracy, 4/5 relevance.
Scenarios: From “What’s gravity?” to “Summarize WWII in 50 words.”
Results: “92% accurate, but 10% of answers were too long.”
Next Step: “Tighten word limits, retest.”

Boom—actionable feedback in a week.

5 Golden Rules for PMs

Keep It Repeatable: Document so your team can rerun it.
Test Broadly: Don’t just check the happy path.
Mix Metrics: Numbers + human gut = fuller picture.
Be Honest: Call out flaws—your users will anyway.
Focus on Users: Eval what impacts them, not just what’s cool.

Why This Matters

As a PM, you’re not just shipping features—you’re shipping trust. A solid AI eval playbook lets you prove your AI delivers, catch risks before they bite, and guide your team to build something users love. Plus, it makes you look like a genius when you present those crisp, data-backed insights to the C-suite.

So, grab this framework, tweak it for your AI, and start testing!

Useful technical implementation reference:

https://cookbook.openai.com/examples/evaluation/getting_started_with_openai_evals

https://github.com/openai/evals/tree/main