Bloomberg's bet on fine-tuning: what it teaches every enterprise about the RAG-vs-fine-tune decision

MBA Training AI · Aug 25, 2026 · 4m · transcript

Bloomberg built a domain-specific large language model from scratch rather than retrieving over generic ones, and the results clarified a decision that still confuses most enterprise AI teams. The logic behind that choice, and where it breaks down for other organizations, is more instructive than the model itself.

Go deeper, free lessons

Choosing the right approach: prompt, RAG, fine-tune, or agent

Retrieval-augmented generation (RAG): giving models your data

Evaluating outputs: how do you know it works?

Cost, latency, and model selection tradeoffs

Scoping, data, and success criteria

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