Explore the cutting-edge world of Retrieval-Augmented Generation (RAG) in this comprehensive course designed to deepen your understanding of both the practical and theoretical aspects of RAG. You will master not only the techniques but also the underlying principles that make these methods effective. Additionally, you will gain the skills to develop dependable "chat with documents" applications, leveraging the latest advancements in Large Language Models (LLMs) and advanced RAG methodologies.
Course Overview
This program progresses from building a basic pipeline to delving into advanced strategies like re-ranking and query expansion. You will also learn to work with a variety of models, including commercial and local ones. The curriculum effectively blends theoretical knowledge with hands-on programming experience using Python. You will become adept at utilizing tools such as LangChain and Streamlit, which are pivotal in the RAG landscape.
Who Should Enroll?
The course is tailored for developers, SaaS product founders, and managers who are eager to swiftly harness the potential of large volumes of textual data. By the course's conclusion, participants will not only have constructed their own fully functioning RAG pipeline but also gained a profound comprehension of strategies necessary to elevate application performance significantly.
What You Will Learn
- Understand the core principles and workings of Retrieval-Augmented Generation (RAG).
- Develop "chat with documents" applications using state-of-the-art LLMs.
- Build and enhance a RAG pipeline with hands-on Python programming.
- Apply advanced techniques, such as re-ranking and query expansion.
- Utilize essential tools like LangChain and Streamlit for RAG applications.
