RAG is the most practical way to give AI models access to your private data without fine-tuning. This guide explains what RAG is, how it works, when to use it, and how to build a simple RAG pipeline.
In this guide
Large language models like Claude or GPT-4 have a training cutoff date and no knowledge of your private data — your internal docs, product catalog, customer history, or company policies. You could fine-tune a model on your data, but that is expensive, slow, and the model still does not "know" data added after training. RAG solves this by retrieving relevant information at query time and injecting it into the prompt.
RAG has two phases. Indexing: your documents are split into chunks, each chunk is converted to a vector embedding (a list of numbers that captures meaning), and stored in a vector database. Querying: when a user asks a question, the question is also converted to an embedding, the vector database finds the most similar document chunks, and those chunks are injected into the LLM prompt as context. The LLM answers based on that retrieved context.
Use RAG when: your data changes frequently (new docs, new products), you need the model to cite sources, your data is large and varied, or you want to avoid re-training costs. Use fine-tuning when: you need the model to behave differently (different tone, follow specific output formats), you want to encode stable procedural knowledge, or you need faster inference. Most business applications should start with RAG — it is faster to implement and easier to update.
Install: npm install @anthropic-ai/sdk openai (for embeddings). Split your docs into ~500 token chunks. Create embeddings: await openai.embeddings.create({ model: "text-embedding-3-small", input: chunk }). Store in a vector DB (Pinecone, Supabase pgvector, or Chroma). At query time: embed the question, find top-5 similar chunks, build prompt: "Based on the following context: [chunks] — Answer: [question]". Send to Claude.
Chunk size matters: 200-500 tokens works well for most use cases. Too large = retrieved context is noisy, too small = chunks lose context. Always include metadata (document title, page number, date) with each chunk so the LLM can cite sources. Implement a reranking step for better accuracy: retrieve 20 chunks, then use a reranker model to select the top 5. Test retrieval quality separately from generation quality.
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