Learning LangChain: A Practical Guide to Building LLM Applications with Python, RAG, and Agents: 1 (Practical LangChain series) - Tapa blanda

HUBSON, MAXWELL

 
9798242968745: Learning LangChain: A Practical Guide to Building LLM Applications with Python, RAG, and Agents: 1 (Practical LangChain series)

Sinopsis

Large Language Models are transforming how modern applications are built—but turning powerful models into reliable, scalable, and production-ready systems remains a major challenge.

Learning LangChain: A Practical Guide to Building LLM Applications with Python, RAG, and Agents is a hands-on guide for developers who want to go beyond simple prompt experiments and learn how to design complete, real-world LLM-powered applications using LangChain.

As LLMs rapidly move from experimentation into production environments, developers must address new concerns around hallucinations, context management, scalability, cost control, and system architecture. This book provides a clear, structured approach to solving those challenges using proven LangChain design patterns.


What This Book Covers

This book teaches you how to assemble end-to-end LLM systems by combining LangChain’s core building blocks, including:

  • Prompt templates and structured output parsing

  • Chains and reusable workflows

  • Conversation memory and context management

  • Retrieval-Augmented Generation (RAG) with vector databases

  • Tool-using and agent-based systems

  • Testing, evaluation, and production deployment strategies

Rather than focusing on isolated prompts or theoretical concepts, the book emphasizes practical implementation, showing how real LLM applications are built, maintained, and scaled.


Master Retrieval-Augmented Generation (RAG)

A major focus of this book is Retrieval-Augmented Generation (RAG)—one of the most effective techniques for improving accuracy and reducing hallucinations in LLM applications.

📘You will learn how to:

  • Load and preprocess documents

  • Split and embed text efficiently

  • Store and retrieve information using vector databases

  • Integrate retrieval seamlessly into LangChain workflows

These techniques are essential for building applications such as chatbots, knowledge assistants, internal search tools, and AI-powered customer support systems.

"Sinopsis" puede pertenecer a otra edición de este libro.