How to build an LLM eval pipeline in GitHub Actions

LLM evaluation pipelines complement conventional CI by detecting quality regressions caused by changes to models, prompts, retrieval, or tool behavior that linting, type checks, and deterministic unit tests cannot catch. Using Braintrust, teams can define versioned datasets, production-linked tasks, and deterministic or model-based scorers to measure qualities such as correctness, groundedness, safety, and tool-use behavior, then run them through GitHub Actions or the bt eval CLI. A typical workflow securely supplies Braintrust and provider credentials, executes evals non-interactively, compares results against a stable baseline, and posts pull-request comments showing aggregate and case-level improvements or regressions. To make evaluations enforceable, repositories must configure required status checks and custom reporters that fail builds when critical score floors, regression limits, or individual safety conditions are violated, while leaving variable signals such as latency and cost advisory unless they are release requirements. The approach recommends fast smoke suites for pull requests, full and scheduled evaluations after merges, repeated trials and aggregate thresholds for non-deterministic outputs, and investigation through immutable experiment records, traces, and output diffs. Production failures can be added to datasets as regression cases alongside their fixes, allowing teams to steadily expand coverage and connect real-world incidents to future release decisions.

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