Real-World Benchmarks

Real-world benchmarks evaluate models on tasks and datasets that mirror production workloads, such as customer support chats or domain-specific documents, rather than academic toy sets.

  Examples (2025)

  Advantages

• Higher external validity than synthetic prompts.
• Reveal long-context and tool-usage capabilities.
• Motivate robustness to messy inputs.

  Challenges

• Costly human evaluation.
• Proprietary data limits reproducibility.
• Rapid model advances quickly saturate scores.

  Implementation Tips

1. Collect anonymized production logs with user consent.
2. Use dual grading: automatic metrics + human review.
3. Update benchmark yearly to avoid over-fitting.