Building Data-Driven Applications with LlamaIndex/LLM大模型应用实战方向
（英文，可编辑的pdf电子文档）

Textbook:Building Data-Driven Applications with LlamaIndex: A practical guide to retrieval-augmented generation (RAG) to enhance LLM applications
Teacher/Author(s): Andrei Gheorghiu

Course Description
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."
From this course, 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.
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.

What you will learn from this course
• Understand the LlamaIndex ecosystem and common use cases
• Master techniques to ingest and parse data from various sources into LlamaIndex
• Discover how to create optimized indexes tailored to your use cases
• Understand how to query LlamaIndex effectively and interpret responses
• Build an end-to-end interactive web application with LlamaIndex, Python, and Streamlit
• Customize a LlamaIndex configuration based on your project needs
• Predict costs and deal with potential privacy issues
• Deploy LlamaIndex applications that others can use