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What are evals, and why do LLM applications need them more than traditional software does?
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Why traditional testing isn't enough
| Traditional software | LLM application |
|---|---|
| Same input, same output | Outputs vary across runs and model versions |
Exact assertions (== 42) | Many valid answers; quality is graded |
| Bugs are reproducible logic errors | Failures are statistical (works 90% of the time) |
| Code changes are the main risk | Prompt, data, model and provider changes all shift behaviour |
What evals enable
- Iteration with confidence: "Prompt v7 improves faithfulness from 86% to 92% without hurting latency."
- Regression detection before users see problems.
- Model selection and migration (Q45).
- Launch decisions and stakeholder trust: evidence instead of demos.
- Monitoring quality in production over time.
Analogy: evals are to LLM apps what unit tests plus A/B tests plus QA audits are to traditional products, combined.
Common mistakes
- Treating a few manual "vibe checks" as evaluation (Q15).
- Measuring only generic benchmarks instead of the application's actual task.
Related
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