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Still think fine-tuning is better than RAG for chatbots? I tested both for a month

I run a small customer support bot for a local HVAC company in Columbus and everybody kept telling me to fine-tune an LLM on their repair manuals. So I spent 30 hours cleaning data and running fine-tuning jobs. Then for comparison I just dumped the same manuals into a vector database with RAG and tied it to GPT-4. The RAG setup caught 92% of questions correctly on the first try while fine-tuning only hit 78%. Fine-tuning also hallucinated model numbers that didn't exist three times in one afternoon. Has anyone else found RAG way easier to maintain when your source data keeps changing?
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cole_flores44
Fine-tuning and RAG solve different problems though. RAG is better for quickly finding specific facts from your docs while fine-tuning is better for teaching the model to follow a certain tone or format.
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