Evals in Himalayas: Inside AI's Global 'Evaluation Gap'

AI benchmark scores are often misleading because they don’t account for variations in model providers, software scaffolds, or specific product layers. Builders frequently fall into ‘model selection hell’ by relying on these scores instead of evaluating models for their unique use cases. Building custom evaluation tools is crucial for understanding true model performance, as demonstrated by examples of misleading demos and the need for tailored testing.

• 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’.
  https://bender.layer3.press/articles/395dd49c-062c-4fe6-ae0f-5e2b4c4d0d18