What is RAG?
Retrieval‑Augmented Generation (RAG) is an advanced technique that enhances LLMs by allowing them to retrieve relevant information from an external knowledge base before generating a response. This reduces hallucinations and provides up‑to‑date, factual answers.
RAG combines information retrieval with text generation. The model first searches a vector database for relevant documents, then uses them as context to generate an answer.
Why RAG Matters
Standard LLMs have a fixed knowledge cutoff (e.g., GPT‑4 knowledge ends in 2023). They also cannot access your private documents. RAG solves both:
- Connects LLM to your own data (PDFs, websites, databases).
- Provides citations and reduces hallucinations.
- Keeps knowledge current without retraining.
How RAG Works (High Level)
1. Indexing: Split documents into chunks, generate embeddings, store in a vector database.
2. Retrieval: For a user query, generate an embedding and find the most similar chunks.
3. Generation: Pass the retrieved chunks plus the original query to an LLM to generate the final answer.
User query → Retrieve relevant chunks from vector DB → LLM (query + chunks) → Answer with citationsTools for RAG
- LangChain / LlamaIndex: Frameworks to build RAG pipelines.
- Vector databases: Chroma, Pinecone, FAISS, Weaviate.
- Embedding models: OpenAI `text-embedding-3-small`, Hugging Face `all-MiniLM-L6-v2`.
Separate Tutorial
RAG is a deep topic with many nuances (chunking strategies, retrieval metrics, reranking, advanced RAG patterns). We have a dedicated RAG Tutorial that covers it from scratch to advanced. Check it out after completing this Generative AI tutorial!
Two Minute Drill
- RAG lets LLMs retrieve external knowledge before generating.
- Reduces hallucinations and provides up‑to‑date answers.
- Works with private documents (PDFs, websites).
- Key components: vector database, embeddings, LLM.
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