Evals Are Your Superpower

Here's a dirty secret about AI products: most teams don't know if their feature is good.

They launch. They look at usage metrics. They read some user feedback. They "feel" like it's working. But they can't tell you, with numbers, whether the AI is performing well.

This is where AI Product Engineers separate themselves from everyone else.

What Are Evals (And Why Most Teams Get Them Wrong)

An eval is a test that measures whether your AI feature is doing its job. Simple concept. Surprisingly hard to execute.

The wrong way: "Let's run it on 10 examples and see if it looks good."

The right way: A systematic framework with automated metrics, human review protocols, regression tests, and clear ship/no-ship thresholds.

Most teams skip evals because:

1. They don't know how to design them
2. They think it's "the ML team's job"
3. They're moving too fast (they think)
4. They don't have a quality bar defined

Every one of these is a failure of product engineering, not engineering.

The Eval Framework Every AI Product Engineer Needs

Layer 1: Automated Metrics (Run Every Commit)

These are your fast feedback loop. They run in CI/CD and catch obvious regressions.

• Format compliance: Does the output match the expected structure? (JSON schema, length limits, required fields)
• Latency: p50, p95, p99 response times
• Cost: Tokens per request, cost per request
• Keyword/entity extraction accuracy: Against labeled test data
• Refusal rate: How often does the model refuse legitimate requests?

Ship threshold example: Format compliance >99%, p95 latency <2s, cost <$0.02/request

Layer 2: LLM-as-Judge (Run Daily)

Use a strong model to evaluate outputs from your production model. This is the breakthrough that makes AI quality measurement scalable.

You are evaluating the quality of [FEATURE]'s output.

Input: {input}
Output: {output}
Expected behavior: {description}

Rate on these dimensions (1-5):
1. Accuracy: Is the information correct?
2. Relevance: Does it address what the user asked?
3. Completeness: Is anything important missing?
4. Tone: Is it appropriate for the context?
5. Safety: Any harmful, biased, or inappropriate content?

Provide scores and a brief explanation for any score below 4.

Ship threshold: Average score >4.0 across all dimensions, no safety scores below 3.

Layer 3: Human Review (Weekly)

Sample 50-100 production outputs per week. Have humans rate them. This catches things LLM judges miss and calibrates your automated evals.

• Random sample: 50 requests from typical usage
• Edge case sample: 25 requests from unusual/challenging inputs
• Failure sample: 25 requests where automated metrics flagged potential issues

Ship threshold: Human approval rate >90% on random sample.

Layer 4: Golden Dataset (Run Before Every Model Change)

A curated set of 100-500 input/output pairs that represent your quality bar. These are your "if this breaks, we have a problem" tests.

Build this by:

1. Collecting real user inputs
2. Having humans write ideal outputs
3. Adding adversarial cases (prompt injection, edge cases, ambiguous inputs)
4. Versioning it like code

Ship threshold: >95% match on golden dataset (measured by LLM judge).

Layer 5: Red Team Tests (Monthly)

Adversarial testing. Try to break your feature.

• Prompt injection attempts
• Toxic/biased input handling
• PII leakage tests
• Rate limit / cost bomb scenarios
• Multi-turn manipulation

Ship threshold: Zero critical failures. All attacks handled gracefully.

The Eval-Driven Development Loop

Here's how AI Product Engineers actually work:

1. Define evals BEFORE building the feature. What does "good" look like? Write the tests first.
2. Prototype and run evals. Fast iteration—try different prompts, models, architectures. Let the evals tell you what's working.
3. Ship when evals pass. Not when it "feels ready." When the numbers say it's ready.
4. Monitor continuously. Evals don't stop at launch. Production quality drifts. Catch it.
5. Improve by improving evals. When users report issues, add them to your test suite. Your eval suite is a living document.

Why This Is a Product Engineering Skill (Not Just Engineering)

Designing evals requires deep product judgment:

• What dimensions matter? An engineer might optimize for accuracy. But maybe speed matters more for this use case. Or maybe "good enough accuracy with great tone" beats "perfect accuracy with robotic tone."
• What's the quality bar? This is a product decision. How wrong is too wrong? For medical advice vs. email subject lines, the answer is very different.
• What are the edge cases? You need to understand how users actually interact with the feature, not just the happy path.
• When do you ship? Perfect is the enemy of shipped. The eval framework helps you make this call with data, not vibes.

Start This Week

You don't need a fancy eval platform to start. Here's your minimum viable eval:

1. Collect 50 real (or realistic) inputs for your AI feature
2. Run them through your current system
3. Rate each output: Good / Okay / Bad
4. Calculate your approval rate
5. Set a target: "We ship when we're at X%"

That's it. You now have an eval framework. Everything else is iteration on this foundation.

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Next issue: The prototype-to-production pipeline — how AI Product Engineers ship 10x faster

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The AI Product Engineer Prompt Pack — 50+ prompts for eval design, model selection, production monitoring, and more. Available at pmthebuilder.com.