Building AI Agents with LLMs, RAG, and Knowledge Graphs

Building AI Agents with LLMs, RAG, and Knowledge Graphs is a hands-on technical guide to the ideas and systems behind modern AI agents. Packt positions it as a practical guide to autonomous and modern AI agents, and the table of contents shows a clear progression from foundational text representation and transformers to LLMs, retrieval-augmented generation, and knowledge graphs.

From LLM foundations to working agents 💻

The early chapters build the groundwork carefully: text encoding, embeddings, deep learning approaches for text, transformers, and the evolution of LLMs. From there, the book shifts into agent design and the tools that make agents useful in the real world.

Retrieval, RAG, and reducing hallucinations

Several chapters focus on retrieval-augmented generation and the practical problem of keeping model output grounded. The book explores naïve RAG, chunking and embedding strategies, advanced retrieval pipelines, hybrid search, query routing, reranking, and response optimization.

Knowledge graphs and structured reasoning

Later sections move into knowledge graphs, showing how structured knowledge can support retrieval and reasoning inside AI systems. That makes this title especially relevant for readers who want to connect LLMs with external data stores, graph-based indexing, and more reliable information workflows.

What readers can expect

• A practical walk from text foundations to modern AI agent architectures
• Coverage of RAG workflows, advanced retrieval, and knowledge graph integration
• Examples that connect theory with implementation-minded AI system design
• Useful context for anyone exploring tool-using, retrieval-aware applications

Ideal for

This ebook will appeal most to developers, data scientists, machine learning practitioners, and technically minded readers who want a structured introduction to AI agents built around LLMs, RAG, and knowledge graphs.

If you are building or evaluating modern AI systems, this is a focused, current title with a strong practical orientation.