Boost RAG with Chroma

Übersicht

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.

Lehrplan

• Building the RAG Pipeline
In this module, you will build the core of a modern, factual AI system. You'll first learn why even powerful LLMs fail and how the RAG architecture solves this by grounding them in real data. Then, you'll get hands-on experience implementing this pattern to construct a functional pipeline that connects a custom knowledge base directly to an LLM.
• Evaluating Hallucination Reduction
With a functional pipeline built, this module focuses on the most critical step: proving its value. You will learn how to systematically test your RAG system, compare its grounded answers against a baseline LLM's responses, and qualitatively evaluate the reduction in hallucinations. This module culminates in a final project where you will formally document your findings.

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Unterrichtet von

LearningMate

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Fachgebiete

Artificial Intelligence