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Retrieval-Augmented Generation (RAG) is one of the most practical techniques for building AI applications that need access to specific knowledge. Instead of fine-tuning a model, you retrieve relevant documents and include them in the prompt.
RAG combines retrieval (finding relevant information) with generation (producing an answer using an LLM). It solves the key problem of LLMs: they only know what was in their training data.
| Approach | Cost | Freshness | Accuracy | Complexity |
|---|---|---|---|---|
| Prompt only | Very low | Static | Limited | Very low |
| Fine-tuning | High | Snapshot | Good | High |
| RAG | Medium | Real-time | High | Medium |
| RAG + Fine-tune | High | Real-time | Highest | High |
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