prompt regression tests llm

Could you clarify what you want by “prompt regression tests llm”?

A few possible meanings:

1. Test prompts for regressions — a suite to ensure an LLM’s answers don’t degrade over time.
2. Regression tests for prompt changes — validate that prompt edits still produce expected outputs.
3. A framework or example code — e.g., Python tests using pytest.
4. A set of test cases — sample prompts and expected behaviors.

If you mean LLM prompt regression testing, here’s a concise starting point:

What it is

You keep a set of “golden” prompts and expected properties, then rerun them whenever:

• the prompt changes
• the model version changes
• tools/function calls change
• retrieval/context changes

What to test

• Output format: JSON valid, schema matches
• Key facts: mentions required items
• Safety/guardrails: no forbidden content
• Tone/style: concise, professional, etc.
• Tool behavior: correct function selected
• Stability: similar answer on repeated runs

Common regression setup

• Store prompts in a test file
• Store expected assertions, not exact text
• Compare using:
  • exact match for structured output
  • regex / schema checks
  • embedding similarity for fuzzy text
  • LLM-as-judge for subjective quality

Example test case

tests = [
    {
        "prompt": "Summarize: The refund policy allows returns within 30 days.",
        "assertions": [
            lambda out: "30 days" in out,
            lambda out: "refund" in out.lower(),
        ]
    }
]

Better approach than exact-match

Instead of expecting one fixed answer, assert:

• contains required facts
• no banned phrases
• valid JSON
• field types and lengths
• business rules

If you want, I can provide:

• a pytest example
• a prompt regression test template
• a CI pipeline setup
• or a list of test cases for your use case