LlamaIndex

A comprehensive guide to RAG - the dominant pattern for building AI applications that answer questions using your own data.

Discover how AI converts text, images, and data into numerical vectors that capture meaning.

Learn how AI understands the meaning behind queries to find relevant results beyond keyword matching.

Learn how to measure and improve the quality, accuracy, and reliability of AI systems.

RAG systems where agents decide what to retrieve, when to search, and how to synthesize multi-step answers.

How to split documents for RAG - fixed-size, recursive, semantic, and document-aware chunking with production trade-offs.

Measuring retrieval quality - recall@k, MRR, nDCG, and building golden test sets for RAG pipelines.

Explore how knowledge graphs enhance RAG pipelines with structured relationships and reasoning.

Discover how knowledge graphs and large language models complement each other - grounding LLMs with structured knowledge.

Measuring LLM output quality - automated metrics, human eval, LLM-as-judge, and building eval pipelines.

Evaluating RAG systems - faithfulness, answer relevance, context precision, and frameworks like RAGAS.

Detecting when LLMs fabricate facts - grounding checks, NLI verification, citation validation, and production guardrails.

Understand how to monitor, trace, and debug AI systems in production.

Understand why LLMs sometimes generate confident but incorrect information, and how to mitigate it.

Designing production AI systems - orchestration layers, model routing, retrieval pipelines, and failure domains.

A structured path from fundamentals to production AI - covering LLMs, agents, RAG, and engineering practices.

A complete guide to building retrieval-augmented generation systems - from embeddings to production.