LangChain

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

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

Understand how LLMs invoke external tools and APIs to extend their capabilities beyond text generation.

Master the art of crafting effective prompts to get better, more reliable results from LLMs.

ReAct, Plan-and-Execute, reflexion, and other architectural patterns for building reliable AI agents.

How AI agents decompose goals into steps, create plans, and adapt when things go wrong.

Short-term, long-term, and episodic memory patterns for AI agents that need to remember across sessions.

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

When to use deterministic LLM workflows vs autonomous agents - and why most production systems are hybrids.

Learn how LLMs generate structured function calls that your application can execute.

Learn how to get LLMs to return data in reliable formats like JSON, XML, or typed schemas.

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.

Systematically testing and comparing prompts - A/B testing, prompt versioning, and regression detection.

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

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

Learn how to add safety constraints and validation to AI outputs to prevent harmful or incorrect results.

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.

Making AI systems fast - streaming, parallel retrieval, model selection, edge caching, and speculative decoding.

Caching LLM responses and embeddings - exact match, TTL strategies, and when caching helps vs hurts.

Cache LLM responses by semantic similarity - serving cached answers for paraphrased queries without exact string matches.

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.

Build autonomous AI agents from scratch - covering tool use, planning, multi-agent systems, and deployment.