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.

Hub guide to evaluating AI systems — four eval layers, golden sets, LLM-as-judge, CI gates, and why public benchmarks are not product readiness.

Foundation models, autoregressive and diffusion generation, and the engineering patterns required to ship reliable applications on probabilistic outputs.

Explore how AI agents autonomously reason, plan, and take actions to accomplish goals.

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

Engineer reliable LLM context budgets across instructions, evidence, history, tools, and output—with retrieval, truncation, summarization, and production observability.

Improve retrieval by rewriting, expanding, or decomposing user queries before search — including query rewriting, multi-query retrieval, query decomposition, and related transformation techniques.

Purpose-built databases for storing, indexing, and querying embedding vectors at scale — including managed options like Pinecone (Database + Nexus knowledge engine).

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.

Structured representations of entities and relationships - the foundation for GraphRAG, enterprise search, and neuro-symbolic AI.

Labeled property graph model - nodes and edges with key-value properties, used by Neo4j and most graph databases.

The declarative query language for property graphs - pattern matching, traversals, and Neo4j queries.

Compare the two dominant knowledge graph models - RDF (W3C standard) and property graphs (Neo4j-style) - and when to use each.

Learn about the Resource Description Framework - the standard for representing knowledge as linked data.

Understand how ontologies define the vocabulary, relationships, and rules for a domain of knowledge.

Explore the Web Ontology Language for defining rich, machine-interpretable ontologies with reasoning capabilities.

Learn how organizations use knowledge graphs to connect data silos and power intelligent applications.

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.

Practical guidelines for designing, building, and maintaining knowledge graphs in production.

Measuring LLM output quality — automated checks, rubrics, LLM-as-judge, human calibration, and CI eval pipelines for generation.

Evaluating RAG systems — faithfulness, answer relevance, context precision/recall, and attributing failures to retrieval vs generation.

Production observability for LLM systems — traces with prompt/completion/tool spans, cost and latency attribution, quality proxies, and PII-safe instrumentation.

Detecting when LLMs fabricate facts — grounding checks, NLI verification, citation validation, and production detection layers.

Why LLMs hallucinate — next-token prediction is not truth-seeking — and how to mitigate with RAG, tools, structured outputs, abstention, detection, and verification.

Reference architecture for modern enterprise AI systems, bringing together retrieval, knowledge graphs, AI agents, LLM gateways, caching, observability, security, and production deployment.

Production RAG architecture — hybrid retrieval, ACL filters, model routing, caching, guardrails, and observability for multi-tenant enterprise systems.

Reference architecture for graph construction, community detection, hybrid retrieval, graph traversal, and query planning.

Reference architecture for grounding, entity resolution, graph traversal, hybrid reasoning, and structured retrieval with LLMs.

Structured path from LLM foundations to production AI — RAG, agents, evaluation, observability, and portfolio-ready engineering habits.

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

From RDF basics to enterprise deployments - a structured path to mastering knowledge graphs.