Safety & Alignment

What is AI Evals (Evaluations)?

Systematic methods for measuring AI model and application quality — the testing framework for AI products.

Definition

AI evals (evaluations) are structured tests that measure how well an AI system performs on a defined set of inputs. Unlike traditional software testing (which checks deterministic behavior), evals measure probabilistic AI outputs — often using rubrics, reference answers, or an LLM judge. Evals are how AI teams know if a prompt change made things better or worse, whether a new model upgrade regressed a use case, and whether their product is safe enough to ship.

Why it matters

Every AI team that ships products needs evals. Without them, changes are shipped blind — you cannot know if the new model is better without test cases. Evals are also how you hold AI vendors accountable, compare competing models for your use case, and detect regressions after updates. PMs who understand evals can make evidence-based decisions about AI product quality rather than relying on vibes.

How it works

An eval suite consists of: (1) test cases — input/expected output pairs or rubrics, (2) an evaluator — human raters, model-based judges, or automated metrics (accuracy, BLEU, ROUGE), (3) a runner that executes every test case against the system, (4) a dashboard showing aggregate pass rates, regressions, and trends. Tools: Braintrust, LangSmith, Promptfoo, and Anthropic's own eval framework.

Examples in practice

Evaluating a customer support bot before launch

100 golden test cases (real customer questions + ideal answers) are run against the bot. The eval measures accuracy, tone, escalation rate, and refusal rate. Only ships when 95%+ pass rate is achieved.

Model upgrade evaluation

When upgrading from Claude 3.5 to 3.7 Sonnet, the eval suite runs both models against all test cases. The upgrade ships only if the new model does not regress on any critical use case.

Common questions about AI Evals (Evaluations)

What are AI evals and why should PMs care?

AI evals are systematic tests that measure AI product quality. PMs should care because without evals, every model change is a guess. Evals give you evidence to make ship/no-ship decisions, measure improvement over time, and hold AI vendors accountable for regressions.

How do you evaluate AI outputs that are subjective?

For subjective outputs (writing quality, helpfulness, tone), the main approaches are: (1) LLM-as-judge — use a frontier model to score outputs against a rubric, (2) human evaluation — A/B preference ratings from target users, (3) reference-based metrics — compare against gold-standard outputs. LLM-as-judge is now the standard for fast, scalable evaluation of open-ended outputs.