RAG is usually the better choice when an AI system needs current, private, or externally sourced information; fine-tuning is better suited to stable specialized behavior, and both can be combined when an application needs current knowledge plus consistent task-specific behavior.
A RAG system can still hallucinate when the correct passage is not retrieved, the retrieved context is incomplete, the source is outdated, or the model generates claims that go beyond the supplied evidence.
Embeddings are the numerical representations that help RAG and semantic search systems find information by meaning rather than exact keyword matches. Here’s how they work, where they fail, and how to build better retrieval.
Document chunking determines what a RAG system can retrieve. Learn how to choose chunk boundaries, preserve context, compare chunking strategies, and evaluate retrieval quality.
Vector databases are retrieval systems designed to search information by similarity and meaning. Learn how embeddings, indexes, filtering and vector search work together in semantic search and RAG.
Retrieval-Augmented Generation (RAG) connects language models to external knowledge so they can retrieve relevant information before generating an answer. This guide explains the architecture, workflow, retrieval process, use cases, limitations, evaluation and production considerations behind reliable RAG systems.
AI memory is not one technology. Learn how context windows, summaries, retrieval, RAG, and persistent memory work together—and why AI sometimes forgets.