I'm building a prompt experimentation loop; what should I use to compare versions and catch regressions?

For a prompt experimentation loop, the best practice is to use a fixed eval set + versioned prompt runs + automatic scoring + regression thresholds.

What to compare

Use at least three kinds of comparisons:

1. Golden / held-out examples

   • A curated set of representative inputs with expected outputs or rubric-based judgments.
   • This is your main regression detector.

2. Pairwise version comparisons

   • Run prompt A vs. prompt B on the same inputs.
   • Ask either:
     • a deterministic scorer, or
     • an LLM judge, or
     • humans for a small sample.
   • Pairwise comparisons are often more sensitive than absolute scores.

3. Production traces / real user cases

   • Include recent anonymized real inputs that previously caused issues.
   • Great for catching regressions that synthetic tests miss.

What to score

Depending on your task, track:

• Task success / correctness
• Format validity
  e.g. JSON parses, schema compliance
• Instruction adherence
• Factuality
• Safety / policy compliance
• Latency
• Cost / token usage

How to catch regressions

Use an eval harness that:

• runs every prompt version on the same test set
• stores outputs with prompt version, model version, temperature, and seed
• compares against:
  • exact match where appropriate
  • rubric scores for subjective tasks
  • structured checks for format
• flags changes when:
  • score drops more than a threshold
  • failure rate increases
  • any critical test fails
  • output distribution shifts

Strong recommendation

Use a mix of:

• hard checks for objective things
• LLM-as-judge or human review for subjective quality
• snapshot tests for outputs that should remain stable

Practical setup

A good loop looks like:

1. Create a small but diverse eval set
2. Define pass/fail criteria
3. Run every prompt revision through the set
4. Compare against the current baseline
5. Auto-block merges if critical regressions appear
6. Periodically refresh the eval set with new failures from production

If you want one simple answer

Use a versioned benchmark suite with pairwise comparisons and regression thresholds.

If you want, I can also suggest:

• a concrete schema for storing prompt versions and eval results,
• a minimal Python eval harness,
• or a scoring rubric for your specific use case.