To stop a prompt regression from merging, run a small evaluation suite when prompt files or their dependencies change, then fail the CI job when defined checks miss their thresholds. Keep a broader dataset for release evaluation. The CI runner or evaluation framework may be free to use, but model calls, hosted-runner allowances, and self-hosting operations can still cost money.
How do I test prompt changes in CI?
Treat the merge gate as executable policy, not just a report: CI runs the evaluation, checks explicit criteria, and returns a failing status when those criteria are not met. That gives reviewers a visible reason to block or revise a change. Promptfoo documents CI threshold checks and the --fail-on-error option; Langfuse documents experiment actions that can fail a job on regression. Promptfoo CI/CD documentation and Langfuse prompt CI/CD guide describe those approaches.
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Build the fast pull-request lane
- Trigger on relevant changes. Configure the workflow to run when prompt files change, and include any referenced dependencies in the path filters. Promptfoo’s GitHub Action can inspect configured prompt dependencies when deciding whether to skip an evaluation, but GitHub path filters must still allow the workflow to start when those dependencies change. See the Promptfoo GitHub Action documentation.
- Choose a deliberately bounded dataset. Include representative common-success cases, known production failures, and a few high-risk edge cases. Langfuse recommends “tens to low hundreds of items” for a PR gate and reserving the full set for release branches. This is vendor guidance, not a guaranteed runtime or a universal optimum. Langfuse’s regression-testing guide.
- Define what counts as a failure. Use deterministic assertions for requirements that can be checked directly, and add model-based judging only where semantic quality needs interpretation. Configure a score threshold or other explicit criterion that causes the job to fail; keep the PR summary concise and retain detailed output for diagnosis.
- Make results reviewable. Attach the commit or run identity to the result, and publish structured output where useful. Promptfoo supports JSON, HTML, and JUnit formats. A hosted GitHub runner starts fresh, so Promptfoo’s disk cache does not carry over between runs unless you pair the action with
actions/cache. See the Promptfoo CI/CD documentation and Action documentation.
Keep release coverage separate
Run the complete evaluation dataset on a release branch, scheduled workflow, or explicit promotion step. The PR suite is optimized for fast feedback; the broader run is for coverage. A clean result from a small dataset does not establish that untested behavior is safe. Langfuse recommends keeping the full set for release branches. Langfuse regression-testing guide.
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How many eval cases should run on every pull request?
There is no evidence-based universal count. Langfuse’s practical recommendation is “tens to low hundreds of items” in the PR gate, with the full dataset reserved for release branches. Use that as a starting range, then tune based on review usefulness, execution time, and the cost of calls—not as a promise of a particular latency or bill.
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Give each case a reason to be there. A compact set can cover:
- Frequent, ordinary requests that should continue to work.
- Production failures, paired where possible with the corrected expected output.
- Anticipated edge cases that expose high-impact risks.
Remove or update cases whose expected behavior has become obsolete. Production traces and domain-reviewed corrections are useful sources for regression examples; an automatically generated test set should not be treated as ground truth without domain review. Langfuse regression-testing guide.
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What should the evaluation score—and what should fail?
A gate can only enforce what its cases and scoring rules represent. Use checks that match the requirement: a deterministic assertion can verify a required field or a prohibited string, while a judge can assess qualities that are harder to express as exact matches. These are different instruments, not interchangeable guarantees.
Deterministic assertions
Prefer a direct check when the requirement has an unambiguous pass condition. Configure assertions and thresholds so that a miss produces a failing CI status rather than a warning that reviewers can overlook. Promptfoo documents assertion configuration and threshold checks in its getting-started guide and CI/CD documentation.
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Model-graded judgments
Use an LLM-as-a-Judge for semantic criteria that simple assertions cannot adequately capture, such as whether an answer follows a nuanced instruction. Judge calls add requests and can introduce evaluator variability, so review examples near a threshold failure rather than treating a score as self-explanatory. Langfuse supports code evaluators and LLM-as-a-Judge; its documentation does not establish a universal mix of deterministic and model-graded checks. Langfuse clarifications.
Can I run LLM evaluations for free in GitHub Actions?
Possibly, but “free server” does not mean a zero-cost evaluation. Separate three budgets: CI execution, evaluation software and hosting, and inference. Hosted CI allowances depend on the applicable account and plan; model providers may bill for evaluation and judge calls; self-hosting shifts operational responsibility to your team. Exact current CI quotas and model prices are not established here, so verify them against your own account and provider before committing to a fixed budget.
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Estimate model requests before enabling a gate
A useful planning formula is:
cases × prompt variants × providers × repetitions + judge calls
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Understand what self-hosting changes
Langfuse states that its core is MIT-licensed and has no usage fee. Its Docker Compose setup is a simple local or virtual-machine option, but it lacks high availability, scaling, and backup functionality. Self-hosting therefore does not remove compute, storage, backup, maintenance, reliability, or external model charges. Langfuse clarifications.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Which CI approach fits the workflow?
| Approach | What the documentation establishes | Best fit to consider |
|---|---|---|
| Promptfoo CLI or GitHub Action | Configuration-driven evaluations, CI thresholds, JSON/HTML/JUnit output, change-aware Action behavior, and optional sharing. Promptfoo CI/CD docs; GitHub Action docs. | A workflow where minimal setup, output retention, provider compatibility, and cache persistence are the main decision points. |
| Langfuse experiment action | Dataset-backed experiments, score thresholds, PR summaries, and prompt-version-triggered workflows. Langfuse prompt CI/CD guide; regression-testing guide. | A workflow where production traces, prompt versions, and evaluation history should be considered together, subject to hosting and governance needs. |
| Self-hosted Langfuse | Core is MIT-licensed with no usage fee; Docker Compose is a simple local or VM setup but does not provide high availability, scaling, or backup functionality. Langfuse clarifications. | A team prepared to operate the infrastructure and assess its reliability and data-handling requirements. |
These are documentation-based distinctions, not a claim that either tool has been tested in a particular repository. Compare the actual setup against your workflow, data policies, and retention needs.
Quick Recap
How do you keep the gate useful over time?
- Turn incidents into cases. Capture the input, the failure, and a domain-corrected expected output so the test protects the behavior that was fixed.
- Add forward-looking edge cases. Cover plausible high-impact failures even if they have not yet appeared in production.
- Inspect threshold failures. Review the example and the scoring rule together; a number without its underlying case can conceal a weak rubric or noisy judge.
- Keep artifacts traceable. Record the commit and run identity, and retain machine-readable results where reviewers need to compare runs.
- Pair offline gates with monitoring. The dataset cannot cover every input or future failure mode, so production monitoring and later evaluation remain complements to the merge gate.
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