Learn to build intelligent, retrieval-powered AI systems using LangChain, LlamaIndex, and real-world RAG workflows
Sub Category
- Data Science
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Objectives
- Design and Build a Retrieval-Augmented Generation (RAG) System Understand how to integrate large language models (LLMs) with retrieval pipelines
- Implement Embeddings and Vector Databases for Semantic Search Learn how to generate and store embeddings using tools like OpenAI, ChromaDB, or Pinecone
- Develop an End-to-End AI Knowledge Assistant Build and deploy a functional AI chatbot using frameworks like LangChain, Streamlit, and FastAPI
- Evaluate and Optimize AI Performance Metrics Measure your assistant’s accuracy, relevance, and user experience using key performance metrics
Pre Requisites
- Basic Python Programming Skills Familiarity with Python syntax and libraries (like pandas, requests, or json) will make it easier to follow along with code demonstrations.
- Curiosity About AI and LLMs A foundational understanding of how Large Language Models (LLMs) like ChatGPT or Llama work conceptually will be helpful, but not mandatory — everything is explained in simple terms.
- Access to a Computer with Internet You’ll need a computer capable of running Python and Jupyter notebooks or VS Code, plus an internet connection to install packages and access APIs.
- Free or Trial Accounts for Tools Some hands-on labs will use free-tier APIs or tools such as OpenAI, LangChain, ChromaDB, and Streamlit — setup instructions are provided in the course.
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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Coupon Code(s)