The PM’s Handbook for AI Evals
Concrete templates, quick wins, and operational practices to make AI features reliable
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AI Product Management - The PM’s Handbook for AI Evals
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Evals are the single most important skill for building reliable AI products. If you are a product manager or an engineer working with LLMs, thinking about evals early is not optional. Evals turn fuzzy expectations into measurable facts you can act on.
Evals let you say whether a model’s outputs meet the product goals, not just whether the model is “smart.”
Good evals expose the specific ways the system fails, so you can prioritize where to fix things.
Evals power three practical functions: they monitor production, protect users with guardrails, and drive product improvement.
Why evals matter?
Why invest time in building evals? Short answer: because LLMs are probabilistic and products are not. Traditional software gives the same output given the same input. LLMs do not. The same prompt can produce different answers. That unpredictability creates four business risks you must manage:
User harm and safety risk
Models can produce incorrect, biased, or unsafe content that harms users or exposes the company to legal or compliance issues.Regressions in user experience
Small changes in prompts, model versions, or retrieval data can suddenly make the product worse. Without evals you only learn about regressions when users complain.Hidden failure modes
Some errors happen rarely but have big impact. A model that usually works but fails on billing emails, medical text, or legal language can cause outsized damage.Slow learning loops
Without measurements, teams guess which fixes will help. Evals make improvements measurable, speeding up effective iterations.
Evals let you move from subjective judgments to objective decisions. Instead of arguing about whether a summary “feels right,” you can point to a metric: percentage of summaries that include the required facts, or the number of extractions that match the email metadata. That lets you answer priority questions clearly:
Is this release safe to ship?
Has the latest model improved or regressed on key user flows?
Which failure mode causes the most user friction and deserves engineering time?
What PMs should do first,
Choose one critical user outcome and make it measurable. Prefer binary checks for clarity. For example: Does the action item list include the sender name? Yes or no.
Run a quick sample of real outputs to understand the problem space.
Use those samples to create simple rules or rubrics you can test automatically.
The conceptual framing
To evaluate LLM systems in a useful way, think in terms of three core gaps and three eval roles. This framing helps you diagnose problems and choose the right evaluation approach.
The three gaps you need to bridge
Comprehension gap: understanding the data you receive
Inputs vary in format, clarity, and noise. You cannot anticipate every input, and you cannot manually inspect every output. The comprehension gap asks: what kinds of inputs does my pipeline see, and how do they affect performance? Example: users send emails with quoted history, attachments, or signature blocks. If your model mistakes a quoted sentence for a new request, that is a comprehension problem.Specification gap: translating product intent into model instructions
The instructions you give the model are a contract. If the contract is vague, the model will interpret it inconsistently. The specification gap asks: have we defined the output format, content, and constraints clearly enough? Example: telling the model to “summarize the email” leaves many choices open. Do you want a two-sentence summary or a bullet list of tasks? Should it include implicit asks? Clarify these to reduce variation.Generalization gap: how well the model applies instructions to new inputs
Even with good prompts and clear inputs, the model can misapply rules on edge cases. The generalization gap asks: does the model behave consistently across the full input distribution? Example: a model might reliably extract names from simple headers but fail when a header uses non-standard punctuation or foreign names.
The three roles of evals
Background monitoring
These evals run passively to detect drift and degradation. They do not block the user flow but alert engineers or PMs when something changes. Use these to watch long-term trends and catch slow problems, like decreasing accuracy after a dataset change.Guardrails
These evals sit in the critical path and enforce safety or formatting constraints. If an output fails, the system can block it, ask the model to retry, or fall back to a safer alternative. Guardrails are essential for high-risk contexts such as legal, finance, or health features.Improvement-driving evals
These are the evaluators you use during development to measure progress and prioritize fixes. Their outputs feed training data, prompt tuning, or architecture changes. These evals should be precise and actionable so you can trace improvements back to specific interventions.
How these concepts interact in practice
Start with the specification gap. Define precisely what you want the model to do. This reduces the number of failures you need to explain later.
Use background monitoring to discover unusual inputs or long-tail failure patterns that your initial samples missed.
When a high-risk failure is identified, add a guardrail to protect users while you iterate on a permanent fix.
Checklist for the conceptual framing
Map one user flow to the three gaps: where might comprehension fail, which parts of your spec are ambiguous, and what edge cases threaten generalization?
For each mapped risk, assign an eval role: will you monitor it, guard it, or create an improvement eval?
Prioritize the top two failure modes by business impact and start designing binary checks for those.
