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RAG vs Fine-tuning — Two Ways to Make LLMs Smarter
When building AI applications, a common question arises: Should you use RAG or fine-tuning?
Both approaches enhance LLM performance, but they address different challenges and operate in distinct ways.
Here’s a breakdown:
1. RAG (Retrieval-Augmented Generation)
Problem it solves:** LLMs lack knowledge of your private data or the latest information.
How it works:
- User sends a query
- A retriever searches a knowledge base
- Relevant documents are retrieved
- The LLM receives the query along with the retrieved context
- The model generates an answer
Knowledge sources can include:**
- PDFs
- Documents
- Vector databases
- APIs
- Web search
- Code repositories
In short:** RAG = LLM + external knowledge retrieval
#AI #LLM #RAG #FineTuning #MachineLearning #GenAI
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