Why Every AI Product Manager Needs to Master AI Evals
Have you been seeing the term ‘AI Product Manager’ everywhere lately? Is it just hype, or are people genuinely ahead of the curve? After all, AI only skyrocketed in visibility after ChatGPT became publicly accessible, right?
NO! People are actually ahead of the game. If you’re interviewing for an AI Product Manager role, there’s one skill that top AI companies expect you to know: how to write AI Evaluations (AI Evals).
According to product leaders at OpenAI (parent company of ChatGPT), mastering AI Evals is becoming a core competency for PMs in the GenAI space.
🗣 Kevin Weil, CPO at OpenAI: “I actually think [writing AI Evals] is going to be one of the core skills for PMs.”
🗣 Mike Krieger, CPO at Anthropic: “If you come interview at Anthropic…one of the things we do in the interview process…we want to see how you think [about AI Evals]…not enough of that talent exists.”
So, if you want to break into AI Product Management, you must understand AI Evals — what they are, why they matter, and how to write them.
What Are AI Evals?
AI Evaluations (AI Evals) are structured processes used to measure the performance, reliability, and accuracy of AI models. Unlike traditional software testing, which focuses on deterministic correctness (right or wrong answers), AI Evals assess subjective and contextual factors such as:
✅ Accuracy: Does the AI generate correct and relevant responses?
✅ Bias & Fairness: Does the model produce ethical and unbiased outputs?
✅ Fluency & Coherence: Is the AI’s response clear, readable, and logical?
✅ Creativity & Originality: Does it provide fresh insights rather than regurgitating data?
Given the non-deterministic nature of AI models (where the same input can yield different outputs), AI Evals help define what “good” actually looks like.
Who Invented AI Evals?
AI Evals have evolved as part of AI and machine learning research, but they became a structured discipline with the rise of large-scale AI systems.
📌 The concept of evaluating AI dates back to Alan Turing’s “Turing Test” (1950) — one of the earliest methods for assessing machine intelligence based on human-like responses.
📌 In modern AI, companies like OpenAI, DeepMind, and Anthropic formalized AI Evals to measure generative AI performance at scale.
📌 Google AI and Facebook AI Research (FAIR) contributed to Eval frameworks for language models, recommendation systems, and image recognition.
Today, AI Evals are standard practice in AI product management, ensuring that models perform reliably, reduce bias, and maintain quality over time.
Why Are AI Evals Required?
Unlike traditional software, AI models don’t always behave predictably. They generate content dynamically, which means they can:
⚠️ Hallucinate (produce false or misleading information)
⚠️ Drift over time (respond differently based on data shifts)
⚠️ Introduce biases (favour certain perspectives unintentionally)
AI Evals help mitigate these risks by providing:
📌 A measurable way to define AI success — beyond just feature releases.
📌 A framework for continuous improvement, refining models over time.
📌 Accountability for ethical AI, ensuring fairness and reducing bias.
How to Structure an AI Eval System
A strong AI evaluation system follows a structured approach:
1️⃣ Create “Goldens” (Ideal Input/Output Examples)
Before testing, define what good outputs look like.
📌 Example: If you’re evaluating an AI chatbot for customer support, an ideal output (golden) should be clear, concise, and empathetic.
💡 Tip: Think like an editor — if the AI wrote a perfect response, what would it look like?
2️⃣ Generate Synthetic Test Data
Once goldens are established, create synthetic test cases based on them.
📌 Example: If your AI summarizes articles, feed it varied inputs — short articles, complex texts, opinion pieces — to see how it adapts.
💡 Tip: Use tools like LLM-generated test cases to scale your evaluation dataset.
3️⃣ Conduct Human Grading
Even with automation, human feedback is essential.
📌 Example: If an AI writing tool generates a blog draft, human reviewers should rate it on clarity, coherence, and engagement.
💡 Tip: Assign reviewers standardized rubrics to ensure consistent scoring.
4️⃣ Build Auto-Raters for Ongoing Evaluation
To scale AI Evals, companies develop auto-raters — algorithms that compare AI outputs to goldens.
📌 Example: OpenAI’s auto-raters help measure GPT models’ performance against predefined standards.
💡 Tip: Auto-raters can’t replace humans completely, but they can automate large-scale evaluations and highlight areas needing human review.
Why AI Evals Matter for PMs
📌 They Define Success for AI Models: Unlike traditional A/B tests, AI PMs must measure subjective quality.
📌 They Ensure AI Doesn’t Drift: Over time, AI models can change — Evals help monitor ongoing performance.
📌 They Improve AI Reliability: A well-structured evaluation framework reduces biases, hallucinations, and inconsistencies.
AI PM Interview Tip: Show Your Evaluation Skills
🚀 If you’re interviewing for AI PM roles, prepare to discuss:
✅ How you would structure AI Evals for a given product
✅ The balance between human review & automation
✅ Metrics you’d use to define AI success
💡 Final Thought: The best AI PMs don’t just build models — they build the frameworks that make AI better over time. Master AI Evals, and you’ll stand out.
