Evals in Himalayas: Inside AI's Global 'Evaluation Gap'
A note on why AI benchmarks don't predict real-world LLM performance, written from a hill with bad wifi.
TL;DR
- Benchmark scores can vary significantly between providers even for the same open model, due to factors not reflected in the score.
- The distinction between a model's general capabilities and its performance in a specific product layer is critical.
- Relying solely on leaderboards can lead to 'model selection hell,' where builders struggle to choose the right model.
- Custom evaluation tools are necessary because off-the-shelf solutions often fail to capture complex, multi-stage AI pipelines.
- Real-world evaluation requires testing with actual inputs and honest assessment of outputs, rather than relying on 'sick demos'.