Fundamentals

The complete guide to AI agent memory: long-term memory for LLM agents, memory architectures from vector stores to knowledge graphs, and how to build one.

Learn how AI knowledge graphs connect entities and relationships to improve search, question answering, generative AI grounding, reasoning, and agent memory.

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.