Building Data-Driven Applications with LlamaIndex. A practical guide to retrieval-augmented generation (RAG) to enhance LLM applications

Generative AI, such as Large Language Models (LLMs) possess immense potential. These models simplify problems but have limitations, including contextual memory constraints, prompt size issues, real-time data gaps, and occasional hallucinations.
With this book, you’ll go from preparing the environment to gradually adding features and deploying the final project. You’ll gradually progress from fundamental LLM concepts to exploring the features of this framework. Practical examples will guide you through essential steps for personalizing and launching your LlamaIndex projects. Additionally, you’ll overcome LLM limitations, build end-user applications, and acquire skills in ingesting, indexing, querying, and connecting dynamic knowledge bases, covering Generative AI and LLM, as well as LlamaIndex deployment. As you approach the conclusion, you’ll delve into customization, gaining a holistic grasp of LlamaIndex's capabilities and applications.
By the end of the book, you’ll be able to resolve challenges in LLMs and build interactive AI-driven applications by applying best practices in prompt engineering and troubleshooting Generative AI projects.