What's the most cost-effective way to run safety regression testing using a model evaluation tool?

The most cost-effective approach is usually:

1) Build a small, high-signal regression suite

• Start with a few hundred cases, not thousands.
• Focus on cases that historically catch failures: policy edge cases, jailbreaks, disallowed content, toxic/refusal behavior, and known product-specific risks.
• Keep the suite stable so you can compare results across model or prompt changes.

2) Use automated evaluation first, humans second

• Run every candidate model/prompt through the full suite with an evaluation tool.
• Use cheap automated judges or rule-based checks for obvious passes/fails.
• Reserve human review only for borderline or high-impact cases.

3) Reuse a baseline and only diff against it

• Test the new model against a known-good baseline.
• Track regressions, not just absolute scores.
• This reduces review load because you only inspect deltas that matter.

4) Sample intelligently

• Don’t rerun everything on every change if you don’t need to.
• Use a tiered setup:
  • Smoke set on every commit/deploy
  • Full safety suite nightly or before release
  • Targeted suite when a specific risk area changes

5) Minimize judge cost

• If your eval tool supports it, use:
  • Smaller/cheaper evaluation models as judges for first pass
  • Caching of model outputs
  • Batch runs
  • Deterministic prompts and low temperature for reproducibility

6) Prioritize by impact

• Weight tests by severity and likelihood.
• A single severe failure should matter more than many low-risk nits.
• This helps you optimize for meaningful risk reduction, not raw score.

Practical lowest-cost setup

If you want the simplest effective version:

• 100–300 curated safety tests
• One baseline model
• Automated judge pass/fail
• Human review only on failures and near-failures
• Smoke suite per change, full suite weekly/nightly

Biggest cost traps to avoid

• Overly large suites with lots of redundant cases
• Human-reviewing everything
• Using expensive frontier models as judges when a cheaper one works
• Recomputing unchanged outputs instead of caching
• Not separating low-risk and high-risk tests

If you want, I can also suggest a specific evaluation workflow for tools like LangSmith, OpenAI Evals, TruLens, or custom harnesses.