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Teams may rewrite the prompt, add context, introduce another reasoning step, adjust the surrounding pipeline, or switch models
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A language model can perform well on a clean benchmark and still struggle with the cases that matter in production.
Key facts
- Benchmarks and curated datasets are useful when prototyping an LLM-based system
- The team encountered these challenges while evaluating an LLM-based system designed to reduce false positives in GitHub secret scanning
- When an LLM system doesn’t perform as expected, the first instinct is often to adjust its technical components
- Before evaluating an LLM system, decide what success means for the user and which guardrails the system must respect
Summary
But as a system moves closer to production, the evaluation problem changes. Real inputs are often ambiguous. Even when offline metrics improve, those results may not translate cleanly into production behavior. The team encountered these challenges while evaluating an LLM-based system designed to reduce false positives in GitHub secret scanning. Secret scanning identifies credentials such as tokens and keys that may have been committed to a repository.