LLMs are trained on static data. Ask one about your internal docs, last week's meeting notes, or a product spec — it has no idea. RAG solves this by fetching relevant content at query time and injecting it into the prompt. The model still does the reasoning; you just give it …

Before you can build a retrieval system, you need to understand what embeddings are and why they exist. Skip this and the rest of RAG won't make sense — you'll be configuring things without knowing what they do.

What an embedding is

An embedding is a list of numbers that represents …

When you build a retrieval-augmented system, most of the tuning work happens before the LLM ever sees a document. How you split text determines what gets retrieved — and bad splits mean bad answers, even with a great model.

The problem with long documents

LLMs have a context window. You can't …

Standard RAG retrieves chunks of text and hands them to an LLM. Graph RAG retrieves a structured slice of a knowledge graph instead — entities, relationships, and the paths between them. That shift changes what kinds of questions the system can actually answer.

Why vector search runs out of road

Chunk …