Learning LangChain: A Practical Guide to Building LLM Applications with Python, RAG, and Agents

By: Hubson, Maxwell

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.

• Format: Paperback
• Language: English
• ISBN13: 9798242968745
• Release Date: January 1
• Publisher: Independently Published
• Length: 280 Pages
• Weight: 0.83 lbs.
• Dimensions: 0.6" x 6.0" x 9.0"