What is RAG (Retrieval-Augmented Generation)?
RAG is a technique that gives AI models access to your own data at query time — so the model answers questions based on your documents, not just its training data.
The problem RAG solves
Language models like Claude and GPT-4 are trained on public internet data. They don't know your internal docs, product catalog, support history, or client records. Without RAG, an AI chatbot can't answer 'What's the return policy on order #4521?' — because that data isn't in its training.
How RAG works
1. Your documents are chunked and converted into vector embeddings (numerical representations). 2. These embeddings are stored in a vector database. 3. When a user asks a question, the system finds the most relevant chunks from the database. 4. Those chunks are included in the prompt sent to the AI model. 5. The model answers based on the provided context.
What RAG enables
RAG is what powers AI chatbots that can answer questions about your specific business: support bots that know your products, internal assistants that know your SOPs, sales tools that know your pricing. Without RAG, these use cases aren't possible with a general-purpose model.
When we use RAG at 2pizza.team
We implement RAG for most AI chatbot and internal assistant projects. We typically use Claude as the model, Pinecone or pgvector for the vector store, and Make or n8n to handle the data ingestion pipeline. A typical RAG setup for a support bot takes 1-2 weeks to build.
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