Fundamentals

GraphRAG adds a knowledge graph to the RAG pipeline so retrieval can follow relationships instead of returning isolated chunks. Learn how the pipeline works, when to use local vs global search, and where GraphRAG earns its complexity over standard RAG.

RAG pairs retrieval with generation so an LLM can answer from external knowledge instead of just its training data. Learn how RAG works, what it solves, and where chunk-based retrieval starts to hit its limits.

LLM hallucinations are fluent, confident-sounding answers that are false or unsupported by any source. Learn what causes them and the detection methods — groundedness checks, self-consistency, LLM judges — that catch them before users act on them.

An AI knowledge base needs more than stored documents. Source context, entity relationships, and plural retrieval strategies are what turn stored data into reliable, agent-ready knowledge.

A knowledge base is a centralized system for storing reusable information — but most fail because of ownership gaps, drift, and no clear sense of what actually belongs in them.

Generative AI and LLMs are not the same thing. Learn the real difference, why architecture matters more than model size, and what memory and retrieval actually do.

There's no single best vector database — the right choice depends on your retrieval workload, deployment model, and whether you need search, RAG, or full AI memory.

Long term memory AI is more than chat history or larger context windows. Learn what agents should keep, retrieve, update, and forget.

Cognee is a memory engine for AI agents that builds a knowledge graph from data and makes it searchable. It's a simple, yet powerful way to build AI agents that can remember and use information over time.