Boost RAG with Chroma
By External Provider
About this course
Boost RAG with Chroma is an intermediate, hands-on course designed for developers and AI practitioners who need to solve one of the biggest challenges with Large Language Models: their tendency to hallucinate. This course moves beyond theory and teaches you how to build a practical, effective Retrieval-Augmented Generation (RAG) pipeline to make your LLMs more trustworthy and enterprise-ready. You will learn the architectural patterns for using a vector database to create an external knowledge base that grounds an LLM's responses in verifiable data. Using a project-based approach, you will implement this pattern, drawing on the popular open-source tools Chroma and LangChain as concrete examples. The course culminates in a hands-on evaluation where you will directly compare your model's answers—with and without RAG—to qualitatively measure the improvement in factuality. You'll leave with a portfolio-ready project and the ability to build safer, more reliable generative AI applications using any set of comparable tools.
What you will learn
- Implement and evaluate a RAG pipeline, using a vector database like Chroma, to reduce LLM hallucinations and measurably improve answer factuality.