Introduction to Retrieval Augmented Generation (RAG)
Learn to build RAG applications using LLMs, LangChain, and Vector Databases. Master prompt engineering, embeddings, and chatbots for real-world automation.
Course Overview
This course provides a hands-on introduction to Retrieval Augmented Generation (RAG), equipping learners with the skills needed to develop intelligent applications that integrate Large Language Models (LLMs) with vector databases. By working with frameworks like LangChain and leveraging tools such as FAISS and ChromaDB, learners will gain practical experience in building RAG-powered solutions, from extracting structured data from invoices to developing an HR policy chatbot with conversational memory. The course is designed to bridge the gap between theoretical AI concepts and real-world applications, making it ideal for Data Scientists, ML Engineers, and Software Developers looking to automate knowledge workflows.
As a stepping stone in the journey of AI-driven automation, this course sets the foundation for more advanced applications in RAG. Learners will be encouraged to explore deeper integrations, optimize model performance, and experiment with new tools in the evolving AI landscape. Whether you're looking to enhance enterprise workflows, streamline information retrieval, or build next-generation AI applications, this course provides the essential knowledge and skills to kickstart your journey into RAG development.
Skills you'll gain
What you'll learn
- Demonstrate Large Language Model capabilities in Natural Language based Automations.
- Demonstrate the use of RAG Applications in a range of problems they can solve.
- Use Vector Databases as a Storage Medium of Language Embeddings in RAG Applications.
- Develop RAG Applications using LLM Frameworks, Models and Vector Databases.
Who Should Attend
This course is designed for Data Scientists, Machine Learning Engineers, AI Engineers, Data Analysts, Software Developers, and IT Engineers who want to harness the power of Large Language Models (LLMs) and Retrieval Augmented Generation (RAG) applications. Whether you're looking to automate knowledge workflows, enhance AI-driven solutions, or integrate LLMs into real-world applications, this course provides the essential skills and tools to get started.
Prerequisites
Basic knowledge of Python programming and an understanding of Large Language Models (LLMs) are recommended for this course. Learners should be familiar with Python syntax, working with libraries, and basic scripting, as well as have an appreciation of how LLMs process and generate natural language. Prior experience with APIs and data handling will be helpful but is not required.
This course is designed to be accessible, but basic familiarity with the domain will help you get the most out of the advanced modules.
Curriculum
Explore the comprehensive, hands-on curriculum designed to build your expertise step by step.
Meet your instructors
Manas Dasgupta
VIEW MY CHANNELFrequently Asked Questions
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