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Data preparation for function tooling is boring
- andreeamiclaus 1y agoFrom building your own Siri, now you learn the boring dataset part that you cannot skip!
- deleted 1y ago[deleted]
- simonw 1y ago> Let's look at the data: 72% of enterprises are now fine-tuning models rather than just using RAG (22%) or building custom models from scratch (6%). This isn't a trend, it's because fine-tuning works when other approaches fail. Where did that data come from? My mental model is still that most companies find fine-tuning an LLM isn't worth the effort compared to promoting with better chosen examples or setting up effective RAG. Am I out of date? On reading further: it looks like this series of posts is specifically about building voice assistants that run on a mobile phone, which need TINY models. From what I understand getting tiny models to perform interesting custom tasks is a challenge that fine-tuning is well suited for.
- simonw 1y agoI think I found the source: A16Z in March 2024: https://a16z.com/generative-ai-enterprise-2024/ https://a16z.com/generative-ai-enterprise-2024/ They surveyed Fortune 500 types for it. The numbers above were from a survey of 70 "AI decision makers" and the question concerned "How are enterprises customizing their models?"
- 3abiton 1y agoI am curious why function calling and not MCP server, don't they serve the same functionality?