This course will equip you with the skills to develop RAG (retrieval-augmented generation) applications using LlamaIndex and Large Language Models (LLMs). You'll explore the integration of LlamaIndex with various data sources and how to fine-tune prompts for sophisticated AI-driven applications. The course starts with the fundamentals of LLMs and the key concepts around prompt engineering, before diving deep into the capabilities of LlamaIndex.

You will first learn the essentials of LlamaIndex and its environment setup, followed by creating your first application. The course progressively takes you through different prompt types, including conversational prompts, and introduces semantic similarity evaluators. You’ll understand the significance of language embeddings, vector databases, and how to work with a Chroma DB or an SQL database to store and retrieve data efficiently. Further, the course will guide you in creating and optimizing query pipelines in LlamaIndex, such as sequential query pipelines and DAG (Directed Acyclic Graph) pipelines, and working with agents and tools. You will build real-world applications, including a calculator using a ReAct agent and a document agent with dynamic tools, demonstrating the versatility of LlamaIndex in various use cases. This course is designed for developers, data scientists, and AI enthusiasts who wish to delve deeper into LlamaIndex for advanced application development. A basic understanding of Python programming and AI concepts is recommended for this intermediate-level course. By the end of the course, you’ll be able to design, build, and deploy powerful RAG-based applications tailored to complex, real-world data needs.