Overview

Build production-ready AI systems with confidence. LangChain for AI Applications: Build Intelligent Agents, RAG Pipelines, and LLM-Powered Systems walks you from first principles to deployment, turning patterns into practical, testable code. You'll learn how to design grounded RAG, safe tool-calling agents, and fast, cost-aware services that your team can ship and maintain. Written in a practitioner's voice and packed with end-to-end examples, this book reflects current best practices: concise prompts, reranked retrieval, typed tools, and CI-backed evaluation (LangSmith). Every blueprint is battle-tested and accompanied by safeguards-token budgets, refusal policies, and draft-then-confirm side-effects-so you avoid common production pitfalls. About the Technology: Large Language Models excel when paired with the right orchestration layer. LangChain provides composable primitives (prompts, chains, tools, memory, retrievers) and a clean path to deployment (LangServe) and evaluation (LangSmith). Combined with vector search (FAISS/Chroma) and modern APIs, you can deliver accurate, auditable, and scalable applications-from knowledge assistants to multimodal agents. What's Inside: - Foundations of LLMs, prompts, chains, tools, and memory - RAG pipelines that cite sources, control cost, and hit latency targets - Agent design with typed tools, function calling, and guardrails - Hybrid retrieval with reranking for higher answer quality - FastAPI/LangServe deployment, Docker, and cloud CI/CD templates - Monitoring, tracing, and experiment tracking with LangSmith - Testing strategies for chains and agents (unit, integration, and rubric-based evals) - Case studies: enterprise knowledge assistant, research summarizer with citations, and a text-plus-vision agent Who this book is for: Engineers, data scientists, and technical product leaders who want reliable, maintainable AI features in real products-whether you're building internal tools, customer-facing assistants, or domain-specific copilots. A working knowledge of Python helps, but each pattern is explained end-to-end. Model APIs evolve quickly and so do costs, limits, and expectations. Teams that standardize on clear patterns-short reranked context, typed tools, measurable evals-ship faster and avoid expensive rewrites. This book gives you those patterns now. Structured in progressive steps, you'll get value in the first weekend (working RAG with citations), the first week (a multi-tool agent with tests), and over the long term (deployment blueprints, evaluation habits, and resiliency playbooks). Instead of scattered tutorials, you get a cohesive toolkit: copy-ready code, minimal but solid infrastructure templates, and checklists that reduce uncertainty in design reviews and post-incident analyses. The result is faster iteration, lower spend, and higher trust from stakeholders. If you're ready to move from experiments to dependable AI products, this book is your operating manual. Add it to your toolkit today and start shipping agents and RAG systems that are accurate, observable, and built to scale.

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