RAG Pipelines Explained

Make your AI smart about your business. RAG is the bridge between generic LLMs and private knowledge.

Training an LLM from scratch is impossibly expensive. Fine-tuning is complex and hard to update. If you want ChatGPT to answer questions about your company's PDF policy documents updated yesterday, you need RAG (Retrieval-Augmented Generation). The core idea is simple: Instead of asking the AI to memorize facts, we give it an "open book" test. We find the relevant pages, show them to the AI, and ask it to answer based on that context.

The Architecture: Indexing & Retrieval

A RAG pipeline starts with Ingestion. We take documents (PDFs, Wikis, Word docs), split them into small "chunks" of text, and enact an "Embedding" process. An Embedding Model turns text into a vector (a list of numbers) that represents its semantic meaning. These styles are stored in a Vector Database (like Pinecone, Milvus, or pgvector).

When a user asks a question, we don't send it to the LLM yet. We first Embed the question and search our Vector Database for the "nearest neighbors"—the chunks of text most semantically similar to the query. This is the Retrieval step.

Generation: The Final Context

Finally, we construct a prompt: "Context: [Insert Retrieved Chunks]. Question: [User Query]. Answer the question using only the context provided." We send this to the LLM. The AI synthesizes the information and generates a natural language answer. This not only provides accurate, specific answers but also reduces hallucinations because the model is grounded in the provided text.

RAG is becoming the standard architecture for Enterprise AI. It respects permissions (you only search documents the user can see) and keeps data fresh (just update the vector store, no re-training needed). If you are building AI apps in 2025, you are likely building RAG.