About the Course
Large Language Models are powerful, but they often struggle to answer questions about private or domain-specific data. Retrieval-Augmented Generation (RAG) solves this limitation by combining document retrieval with LLM-based response generation.
This course teaches developers how to build a RAG system completely from scratch rather than relying on high-level frameworks that hide the implementation details. You will understand every stage of the pipeline, including document preparation, chunking strategies, embeddings, similarity search, and prompt design.
The course begins by explaining why LLMs hallucinate and how parametric and non-parametric knowledge differ. Then it gradually walks through each part of the RAG pipeline before combining them into a complete working system.
By the end of this course, you will be able to build your own end-to-end RAG pipeline capable of answering questions from external documents and enterprise knowledge sources.
Course Objectives
By the end of the course you will be able to:
Understand why Retrieval-Augmented Generation exists and when to use it
Explain the difference between RAG, fine-tuning, prompt engineering, and tool usage
Prepare documents and attach metadata for retrieval pipelines
Design effective chunking strategies for document segmentation
Generate vector embeddings and perform similarity search
Implement cosine similarity search manually
Design RAG prompt templates to prevent hallucinations
Build a complete end-to-end RAG system applied to real data
Prerequisites
Basic Python programming knowledge
Basic understanding of Large Language Models
No prior experience with embeddings or vector databases required
Your Instructor
Codersarts Team
Codersarts Team
