How to Build a Retrieval-Augmented Generation (RAG) Pipeline

Retrieval-Augmented Generation (RAG) connects an LLM to external knowledge bases. Rather than fine-tuning a model on custom documents, you retrieve relevant information from your data and insert it into the LLM's prompt context to generate highly accurate answers.

Break large documents (like PDFs or markdown guides) into smaller, overlapping text segments (e.g., chunks of 500 characters with 50 characters overlap) so that the semantic meaning of each segment is preserved.

Pass each text chunk through an embedding model (like OpenAI's text-embedding-3-small) to convert the text into numerical vectors that represent semantic meaning.

Store your generated embeddings alongside the original text chunks in a vector database like Pinecone, Chroma, or pgvector, creating a indexed directory for semantic search.

When a user asks a question, generate an embedding of their query. Run a cosine similarity search in the vector database to retrieve the top-K most similar text chunks (typically K=3 to 5).

Construct a prompt template that provides the retrieved context chunks and the user's original question. Feed this prompt into the LLM (like GPT-4o or Gemini 1.5 Pro) to synthesize a clean, grounded answer.