Overview
About this program
Large language models are powerful but they hallucinate, and they don't know your data. Retrieval-Augmented Generation (RAG) solves this by grounding AI responses in real, up-to-date, and private information sources.
This program teaches you to build complete RAG pipelines: chunking and embedding documents, storing them in vector databases, retrieving the right context, and generating accurate, grounded responses.
By the end, you will be able to design and deploy production-style RAG applications, the architecture behind most real-world enterprise Gen AI products today.
What You'll Gain
Learning outcomes
Understand how large language models generate text and where they fall short
Master retrieval-augmented generation (RAG) to ground AI outputs in real data
Work with embeddings and vector databases to power semantic search
Design and build end-to-end RAG pipelines for real-world use cases
Evaluate and improve retrieval accuracy and generation quality
Deploy a working RAG-powered application as a capstone project
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