What should I use for automated prompt regression tests?

For automated prompt regression tests, the best choice depends on what you mean by “regression”:

If you want to test prompt behavior over time

Use a prompt evaluation framework that can:

• run the same prompt against fixed test cases
• compare outputs to expected criteria
• score results automatically or with an LLM judge
• fit into CI

Good options:

• promptfoo — very practical for prompt regression testing, CI-friendly, supports assertions, model comparisons, and evals.
• LangSmith — good if you already use LangChain or want tracing + evals.
• OpenAI Evals — useful if you want a more custom benchmark-style setup.
• DeepEval — good for LLM app testing with unit-test-like structure.
• TruLens — strong for evaluating LLM app behavior and RAG systems.

My default recommendation

If you’re just getting started, use promptfoo.

Why:

• easy to set up
• works well for “before/after” prompt changes
• supports exact-match, regex, JSON schema, similarity, and LLM-based assertions
• integrates with GitHub Actions and other CI systems
• model-agnostic

What to test

Typical regression tests include:

• format: valid JSON, required fields present
• content: key facts or constraints included
• style/tone: concise, formal, avoids forbidden wording
• safety: no disallowed content
• tool behavior: correct function call selection
• RAG: answer grounded in retrieved context

Practical setup

A solid workflow is:

1. Create a small set of representative prompts
2. Define expected outputs or assertions
3. Run them in CI on every prompt/model change
4. Fail the build if scores drop below threshold

Quick rule of thumb

• Prompt-only apps: promptfoo
• LangChain apps: LangSmith
• Research/benchmarking: OpenAI Evals
• Python unit-test style: DeepEval
• RAG-heavy evaluation: TruLens or LangSmith

If you want, I can recommend a tool based on your stack, like:

• Python vs JavaScript
• API-only vs LangChain/LlamaIndex
• simple prompt tests vs RAG/tool-use tests