Leaderboards are useful for discovery. They are a weak way to decide what your product should run in...
Leaderboards are useful for discovery. They are a weak way to decide what your product should run in production.
The model that wins a public benchmark may not be the model that fits your workload, latency target, budget, retry behavior, or failure tolerance.
A better first step is smaller and more boring: build a model selection logbook.
The model-selection mistake
Many AI products start model selection like this:
Most teams do not need the perfect AI model on day one. They need a first model they can...
Researchers from Inclusion AI and Ant Group proposed a new LLM leaderboard that takes its data from real, in-production apps.
Companies are starting to choose AI models by task, cost and control, not just leaderboard rank.
Most AI apps start with one model provider. That is usually the right choice. For a first version,...
One AI Vendor Is a Single Point of Failure. Treat It Like One. The AI model you built your...
A practical guide for AI app builders to define baseline metrics for cost, quality, latency, adoption, and business impact before…
