RAG Explained: How Retrieval-Augmented Generation Actually Works

Retrieval-Augmented Generation (RAG) is the technique that makes AI useful for your specific data. Instead of relying solely on the model's training data, RAG retrieves relevant documents and uses them as context.

The RAG Pipeline

Step 1: Document Ingestion

First, your documents are split into chunks and converted to vector embeddings:

python
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splitter = RecursiveCharacterTextSplitter( chunk_size=1000, chunk_overlap=200 ) chunks = splitter.split_documents(documents) ```

Step 2: Vector Storage

Store embeddings in a vector database for fast similarity search:

python
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vectorstore = Chroma.from_documents( documents=chunks, embedding=OpenAIEmbeddings() ) ```

Step 3: Retrieval & Generation

When a user asks a question, retrieve relevant chunks and pass them to the LLM:

python
1retriever = vectorstore.as_retriever(search_kwargs={"k": 5})
2relevant_docs = retriever.get_relevant_documents(query)

Key Decisions

When to Use RAG

• Internal documentation Q&A
• Customer support with knowledge base
• Research over large document collections
• Any task requiring specific, up-to-date information