Overview

In the rapidly evolving world of Large Language Models (LLMs), those who can build structured, reliable, and intelligent workflows are leading the next wave of AI innovation. Whether you're a machine learning engineer, backend developer, data architect, or tech founder, this book gives you a crystal-clear blueprint to go from toy prototypes to production-ready LLM systems. Building LLM Workflows cuts through the noise to deliver what actually works in practice covering LangChain orchestration, LangGraph's stateful flows, Retrieval-Augmented Generation (RAG), OpenAI tool integration, and more. With practical insights and clean code patterns, you'll not only understand the moving parts but learn how to connect them into scalable, modular AI pipelines. This isn't just about building this is about building better: faster iteration, more reliable behavior, safer deployments, and smarter agent coordination. If you're tired of scattered tools and generic tutorials, this book delivers the architectural clarity and hands-on depth you've been looking for. What You'll Learn Inside: Design modular LLM systems with LangChain, LangGraph, and real-world APIs from ideation to deployment. Build robust RAG pipelines with hybrid retrieval strategies, chunking methods, and vector stores like Chroma and FAISS. Manage stateful interactions with LangGraph's node-edge logic and event-driven workflows. Engineer scalable agent behaviors using prompt templates, retries, branching, and memory control. Implement production-grade safeguards against hallucination, prompt injection, and tool misuse.

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