Day 53 of 60
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AI-system specific
Prompt eval suites (offline)
A prompt edit silently regresses on inputs you no longer remember. Held-out test sets are how you catch the regression before customers do.
ProblemA prompt edit silently regresses performance on inputs you no longer remember.
How it works
Maintain a held-out test set with golden inputs and assertions. Run on every prompt change. Fail the PR if regression exceeds tolerance.
What it catches
Prompt regressions, model-version drift, distribution shift. Cheaper than running production traffic to discover the same.
Tools
inspect_ai (UK AISI) · OSS DSPy · OSS Promptfoo · OSS OpenAI Evals · OSS DeepEval · OSS
Verdict by project size
Small
Rec
Medium
Must
Large
Must
Extra-large
Must
Cost
| Project size | Setup | Maint / mo | Tool / mo | CI / run |
|---|---|---|---|---|
| Small <10k LOC | 4h | 1h | $0 | +2m |
| Medium 10–100k LOC | 2d | 5h | $0 | +5m |
| Large 100k–1M LOC | 8d | 25h | $200 | +15m |
| Extra-large >1M LOC | 30d | 100h | $2k | +30m |
Setup = engineer-days to first useful run ·
Maint = engineer-hours / month at steady state ·
Tool = out-of-pocket $ / month ·
CI = minutes added (or saved) per pipeline run
Lifecycle & ownership
When in lifecycle
Test Operate Observe
Per merge · Runs after merge to main; nightly heavy jobs.
Who owns it
ML / AI Engineer
Models, evals, drift, guardrails
Collaborates with: Developer, Security / AppSec
Reference implementations
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promptfoo examples
Prompt regression suites and provider comparison examples.
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OpenAI Evals examples
Prompt and model regression examples with reusable eval templates.
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DSPy examples
Programmatic prompt optimization and evaluation examples.
Quick check
Prompt eval suites primarily defend against…
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