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Build Your First RAG Pipeline

Design and implement retrieval-augmented generation (RAG) pipelines using LangChain and ChromaDB. This course covers indexing strategies, query construction, and evaluation metrics for optimal RAG performance. Upon completion, you'll be able to build and deploy custom RAG pipelines tailored to specific knowledge domains.

Preview First Lesson — FreeView Syllabus
Lessons
8
Price
Free
Course Curriculum

What you'll learn

4 Modules
8 Lessons
~40m total
01
01
Module 01
RAG Pipeline Fundamentals
This module introduces the core concepts of retrieval-augmented generation (RAG) pipelines. We will cover the architecture of a RAG system, the role of vector databases, and the integration with Large Language Models using LangChain.
2 lessons10 min
0%
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02
02
Module 02
Building and Evaluating RAG
This module focuses on building and evaluating a RAG pipeline using ChromaDB and LangChain. We will learn how to construct effective search queries, evaluate the performance of your RAG pipeline, and optimize it for improved accuracy and response time.
2 lessons10 min🔒 Locked
0%
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03
03
Module 03
RAG Pipeline Fundamentals
This module introduces the core concepts of Retrieval-Augmented Generation (RAG) pipelines. You'll learn how RAG enhances Large Language Models (LLMs) by grounding them in external knowledge sources. We will use Langchain's document loaders and ChromaDB for vector storage.
2 lessons10 min🔒 Locked
0%
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04
04
Module 04
Building and Optimising RAG
This module focuses on building and optimising a complete RAG pipeline. You'll learn how to query ChromaDB, integrate with an LLM, and evaluate the performance of your pipeline. We will cover techniques for improving speed and accuracy.
2 lessons10 min🔒 Locked
0%
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Build Your First RAG Pipeline
16/05/2026
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