Hey folks! I'm diving into some casual research for my project and I'd love to hear your thoughts. For those of you working in AI/ML or developing applications, do you ever think about using a smaller, domain-specific model instead of relying on a large general-purpose model like a big LLM? I'm curious if having something fine-tuned specifically for a certain industry would be more beneficial or cost-effective for you, as opposed to using a one-size-fits-all approach. What do you think? Would a mini model serve you better, or do the larger models meet your needs just fine?
2 Answers
Absolutely! Especially in complex but deterministic multi-step processes, it makes a lot of sense. You can have multiple agents, each with its own specialized model for their task. This way, you can choose the most cost-effective model for each function within the workflow. Have you ever tried implementing this setup? What’s been the biggest challenge for you, like selecting the right models or ensuring stability in real applications?
Isn't that the whole point of RAG? It really focuses on getting the right information when you need it. But honestly, I’ve been thinking more on the modeling side itself. It could be more about having your reasoning adapted for a specific area, rather than leaning on a massive general model all the time. Do you think RAG handles most of the concerns, or are there definitely situations where the base model makes a difference?
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