What is RAG?

At its core, RAG is an AI architecture that allows you to fetch relevant, up-to-date, or proprietary information from an external database and feed it to an LLM before it generates a response. Instead of relying purely on the model's internal weights to answer a question, you are explicitly handing it the facts and saying, "Answer the user's question using only this information."

How RAG Works A standard RAG pipeline is broken down into two main phases: Data Ingestion and Retrieval/Generation.

1. Data Ingestion (Preparation)

Chunking: You take your knowledge base (PDFs, internal wikis, documentation) and break it down into smaller, manageable text chunks.

Embedding: Each chunk is passed through an embedding model to convert the text into vector representations (arrays of numbers capturing semantic meaning).

Storage: These vectors are stored in a Vector Database (like Pinecone, Weaviate, or pgvector).

2. Retrieval and Generation (Runtime)

Query Embedding: When a user asks a question, that specific query is instantly converted into a vector using the same embedding model.

Semantic Search: The vector database performs a similarity search, retrieving the top K chunks of text that are most mathematically similar to the user's query.

Prompt Augmentation: The retrieved text chunks are injected into the LLM's system prompt alongside the user's original question.

Generation: The LLM reads the context and generates a grounded, accurate response.