Boost RAG with Chroma

This course is part of Chroma, Weaviate & Production RAG Deployment Specialization

Instructor: LearningMate

Access provided by L&T Corp - ATLNext

2 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

4 hours to complete

Flexible schedule

Learn at your own pace

2 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

4 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Implement and evaluate a RAG pipeline, using a vector database like Chroma, to reduce LLM hallucinations and measurably improve answer factuality.

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

2 assignments¹

AI Graded see disclaimer

Taught in English

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This course is part of the Chroma, Weaviate & Production RAG Deployment Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 2 modules in 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.

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.

What's included

2 videos1 reading1 assignment1 ungraded lab

2 videos Total 12 minutes

From Hallucination to Reality: Grounding AI with RAG 7 minutes
Building a RAG Pipeline with LangChain and Chroma 5 minutes

1 reading Total 8 minutes

The RAG Architecture Explained 8 minutes

1 assignment Total 20 minutes

Knowledge Check: RAG Components 20 minutes

1 ungraded lab Total 60 minutes

Hands-On: Indexing a Knowledge Base into a Vector Store 60 minutes

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.