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To stop a prompt regression from merging, run a small evaluation suite when a prompt or one of its dependencies changes, then fail the CI job when a defined check misses. Keep broad evaluation for a release or scheduled workflow. A free CI runner does not guarantee a free evaluation: model calls may be billed, hosted-runner limits depend on your account, and self-hosting takes operational work.
How do I test prompt changes in CI?
Make the merge gate an executable policy: a workflow runs the evaluation, applies explicit pass criteria, and returns a failing status when a criterion is not met. That status gives reviewers a concrete signal in the pull request rather than asking them to infer quality from a prompt diff.
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For example, Promptfoo documents CI threshold checks and a --fail-on-error option; its GitHub Action can also skip evaluation when configured prompt dependencies have not changed. Langfuse documents dataset-backed experiments, score thresholds, and pull-request summaries. These are documented capabilities, not results from a hands-on comparison. See Promptfoo’s CI/CD integration guide, the Promptfoo GitHub Action, and Langfuse’s prompt CI/CD guide.
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Limit the fast workflow to relevant changes, such as prompt files and configuration or data files they rely on. If using GitHub path filters, include those dependencies in the filter: an action cannot inspect a change if the workflow never starts. Promptfoo’s Action documentation describes dependency-aware skipping after the action is invoked.
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Choose checks that can explain a failure
Use deterministic assertions for requirements that can be checked mechanically, such as required content or a structured-output constraint. When the requirement is semantic, an LLM judge may help, but it adds model requests and its judgments may vary. Treat these as different instruments, inspect cases near a threshold, and do not assume a universal mix of assertion types. Promptfoo documents assertions and thresholds in its getting-started guide; Langfuse documents code evaluators and LLM-as-a-Judge in its clarifications.
Make the result reviewable
Set a clear failure condition and provide a concise job summary. Keep a structured result when the summary alone is not enough to diagnose a failure. Promptfoo supports JSON, HTML, and JUnit output, among other CI features. Its Action cache is not retained between fresh GitHub-hosted runners unless you configure persistence with actions/cache; see the Action documentation.
How many eval cases should run on every pull request?
Keep the PR dataset deliberately small and stable. Langfuse recommends “tens to low hundreds of items” for PR gates and reserving the full dataset for release branches. This is operational guidance, not a measured guarantee of runtime or cost; the appropriate size depends on your cases, models, and workflow.
Give each case a job in the gate. A compact set should cover:
- Common successful requests, to catch breakage in ordinary use.
- Known production failures, paired with corrected outputs or expected behavior.
- A few high-risk edge cases that are plausible even if they have not yet occurred.
Langfuse’s regression-testing guide recommends deriving cases from production traces and domain-corrected outputs. Add anticipated risks as hand-written cases, and periodically retire or update examples that no longer represent the product. An automatically generated test set is not ground truth until someone with domain knowledge has reviewed it.
What belongs in a broader release evaluation?
The fast gate and the full evaluation have different purposes. Run the complete dataset on a release branch, in a scheduled workflow, or as an explicit promotion step. A small PR suite can catch recurring, selected failure modes; it cannot demonstrate that unrepresented behavior is safe. Continue to use production monitoring or later evaluation to find gaps, then turn important incidents into regression cases.
How should you budget model calls and CI?
Estimate inference demand before enabling the gate. A useful planning model is:
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cases × prompt variants × providers × repetitions + judge calls
This is a way to count likely requests, not a quote for cost. Some evaluations make additional calls for grading; Promptfoo’s getting-started documentation illustrates how multiple models and test cases can produce multiple generation and grading calls. Caching may reduce repeat provider calls, but on fresh hosted runners it requires cache persistence configuration.
Keep the runner allowance separate from inference charges. The sources cited here do not establish current GitHub free-minute quotas or provider rates, and the applicable limits depend on account and provider configuration. Check both before promising a fixed zero-cost total. Self-hosting can remove a software usage fee for core Langfuse according to its clarifications, but it does not remove compute, storage, backups, maintenance, or external model usage.
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| Option | What the documentation establishes | Consider when deciding |
|---|---|---|
| Promptfoo CLI or GitHub Action | Configuration-driven evaluations, threshold checks, JSON/HTML/JUnit outputs, change-aware Action behavior, and optional sharing. See CI/CD integration and GitHub Action. | How you want to configure tests, retain results, persist caches, and connect to providers. |
| Langfuse experiment action | Dataset-backed experiments, score thresholds, pull-request summaries, and prompt-version-triggered workflows. See prompt CI/CD and regression testing. | Whether production traces, prompt versions, and evaluation history should share a system, and what hosting or governance you need. |
| Self-hosted Langfuse | Langfuse says its core is MIT-licensed with no usage fee. Its documentation describes Docker Compose as a simple local or VM setup that lacks high availability, scaling, and backup functionality. See clarifications. | Who will operate the infrastructure, required reliability and data handling, and whether hosted convenience is worth paying for. |
Can LLM evaluations run for free in GitHub Actions?
Possibly, for a particular configuration and account, but “free server” is not the same as “free evaluation.” CI execution allowances vary by account and plan; model-provider usage may be billed; and a self-hosted evaluation service has infrastructure and maintenance costs. Verify the current terms for your runner account and provider rather than relying on a general free claim. Tool versions and plan entitlements can change, so check the linked product documentation when configuring the workflow.
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