Retrieval-Augmented Generation (RAG) is the most practical approach to making LLMs work with your specific data. Instead of fine-tuning an entire model, RAG retrieves relevant documents and feeds them as context to the LLM.
Architecture Overview
A typical RAG pipeline consists of three stages: document ingestion (chunking and embedding), retrieval (vector similarity search), and generation (LLM synthesis of retrieved context).
Key Considerations
- Chunk size: Too small and you lose context; too large and you dilute relevance.
- Embedding model: Choose based on your domain — general models work well for most cases.
- Retrieval strategy: Hybrid search (combining semantic and keyword) often outperforms pure vector search.
