Building Efficient Pipelines for LLM Workflows in Agentic RAG

What if the real breakthrough in artificial intelligence isn’t a bigger model-but a smarter pipeline?
This book is your complete guide to designing, optimizing, and scaling agentic Retrieval-Augmented Generation (RAG) systems-the architecture that transforms large language models from passive text generators into active, reliable problem-solvers.
In Building Efficient Pipelines for LLM Workflows in Agentic RAG, you’ll learn how to construct intelligent pipelines that combine retrieval, reasoning, planning, and verification into one cohesive system. This is not theory-it’s a step-by-step framework for building adaptive, production-grade AI workflows that are fast, grounded, and self-correcting.
What You’ll Learn

How to design multi-stage agentic pipelines that decompose tasks, verify answers, and handle complex reasoning.
Proven methods for integrating vector search, graph retrieval, and hybrid retrieval strategies.
Real implementation examples using LangChain, LangGraph, and LlamaIndex.
How to build observability and CI/CD into your AI stack for consistency and control.
Strategies for cost optimization, latency reduction, and self-healing AI pipelines.
Domain-specific workflows for enterprise search, healthcare, legal, and technical research use cases.

A look ahead at post-RAG architectures, including neural-symbolic reasoning, long-context models, and autonomous agent ecosystems.
Whether you’re optimizing internal LLM pipelines or deploying full-stack agentic systems, this guide gives you the tools to move beyond demos and prototypes-toward AI infrastructure that’s grounded, reliable, and ready for production.
If you’re ready to transform your LLM workflows into efficient, adaptive pipelines that think before they generate-this book will show you how.