AI Evals: Building Test Suites for LLM Applications (2026)

You can't ship an AI product without evals. Here's how to build eval sets, what to measure, and how to iterate models without regressing quality.

AI Evals: Testing LLM Outputs Without Human Review at Scale

Traditional software has unit tests and integration tests. AI applications need evals — automated tests that grade the quality of LLM outputs against expected criteria. Without evals, you can't safely change prompts, swap models, or upgrade to a newer version — every change is a coin flip on quality. Ship-blocking evals separate serious AI companies from prompt-tinkering.

What evals measure

Correctness: does the output match a known ground truth (for tasks with objective answers). Structure: does the output follow required format (JSON schema, specific fields). Safety: does the output avoid prohibited content (PII, harmful advice, off-topic responses). Quality: does the output meet subjective quality bars (usually LLM-as-judge or human review). Latency and cost: performance metrics per output. Different tasks need different eval mixes.

Building an eval set

Start with 20-50 real production examples covering common cases and edge cases. Add: examples where past prompt versions failed (regression tests), adversarial inputs (users trying to break the system), and long-tail examples that represent 5% of traffic. Grow the set to 200-1000 examples over time. Each example: input, expected output or evaluation criteria, tags for filtering (task type, difficulty, priority).

LLM-as-judge for subjective quality

For outputs where correctness isn't binary (summaries, chat responses, creative writing), use a stronger model to grade outputs against a rubric. Prompt the judge with the input, the output, and the criteria; get a 1-5 score with reasoning. Correlate judge scores with human labels on a sample to validate the judge's accuracy. Well-tuned LLM judges reach 80-90% agreement with human labelers for many tasks.

CI/CD integration

Run evals on every prompt or model change, before deploying. Ship-blocking thresholds: >90% pass rate on critical evals, >X% average quality score. Track eval performance over time — regressions surface quality degradation from model updates you didn't cause. When an eval fails, either fix the change or add the failure case to the eval set as a permanent test. This is how AI systems get better without regressing.

Frequently asked questions

How many evals do we need to start?
20-50 examples covering common tasks. Perfect is the enemy of shipped — start with a small set, use it to catch obvious regressions, grow it as production surfaces new edge cases. Waiting to build a comprehensive eval set delays shipping the eval discipline.
How often do we run evals?
On every prompt or model change (blocking), weekly on production samples (monitoring), monthly deep-dives on failure patterns. Automated evals in CI/CD prevent regressions; production sampling catches issues evals miss.
What tools should we use?
Options: OpenAI Evals (open source), Braintrust, Langfuse, Weights & Biases Weave, or custom. Custom is usually fine at seed stage — a few Python scripts calling your LLM API and grading outputs. Move to a hosted tool when you have >5 people iterating on prompts.

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