Building AI Agents with LLMs, RAG, and Knowledge Graphs
A practical guide to autonomous and modern AI agents
Created by Salvatore Raieli, Gabriele Iuculano
Explore how to build intelligent AI agents that combine large language models with real-world data and structured knowledge. Gain practical experience integrating retrieval-augmented generation, knowledge graphs, and autonomous agent techniques to solve complex problems.
Packt | Jul 2025 | 566 min
What You Will Learn
You will start by understanding the foundations of large language models and their best practices. Then, you will move on to building retrieval-augmented generation systems and connecting knowledge graphs to language models using Python. Finally, you will create and orchestrate AI agents that combine planning, tool use, and knowledge retrieval for advanced applications.
Key Features
- Design RAG pipelines to connect language models with external data sources
- Build and query knowledge graphs for context-rich, accurate AI reasoning
- Develop autonomous agents that plan, retrieve, and act to complete tasks
Target Audience
This content is ideal for data scientists and researchers who want to create and deploy AI agents for real-world tasks. A basic understanding of Python and generative AI concepts will help you get the most value. It is also well suited for experienced professionals seeking to master the latest developments in language model-based AI solutions.
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