Rag vs Long Context vs Fine Tuning When to use ...
Most people still don't know the difference between RAG, long context, and fine-tuning. So let me explain it simply for you. RAG gives your model an open book exam. For every question you ask, the large language model can use actual company data in its response and cite that so you know exactly where the information is coming from. Long context is just putting more tokens into a single call. This is really simple to implement and sounds powerful, but even the best models today still degrade in performance after a certain amount of tokens. And you could never fit an enterprise database into a million tokens anyways. Fine-tuning actually changes your model's style, tone, and structure, not its knowledge, retraining every time your data change would bankrupt you overnight. Pros actually combine RAG and fine-tuning with something called RAF. If you have an hour and a half, you should watch this video, which will break down all three and when to use them.
Summary
The video explains the differences between RAG, long context, and fine-tuning in AI models, highlighting their uses and limitations.
Key Points
- RAG allows models to use real company data for responses.
- Long context increases token count in a single call.
- Performance degrades after a certain number of tokens.
- Fine-tuning alters the model's style and tone, not knowledge.
- Combining RAG and fine-tuning is known as RAF.
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Repurpose Ideas
- LinkedIn post: Key differences between RAG and fine-tuning
- Tweet: When to use RAG vs. long context in AI
- Checklist: Steps to implement RAG in your AI model
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