Introduction to Retrieval Augmented Generation (RAG)
In this course, we start with the concepts and use of Large Language Models, exploring popular LLMs such as OpenAI GPT and Google Gemini. We will understand Language Embeddings and Vector Databases, and move on to learn LangChain LLM Framework to develop RAG applications combining the powers of LLMs and LLM Frameworks.
The capabilities of LLMs are not to be kept confined within the tools like ChaGPT or Google Gemini or Anthropic Claude. You can leverage the powerful Natural Language Capabilities of LLMs applied on your organizational data to create amazing automations and applications that are called Retrieval Augmented Generation or RAG Applications.
Some of the key components of the course are learning prompt Engineering for RAG Applications, working with Agents, Tools, Documents, Loaders, Splitters, Output Parsers and so on, which are essential ingredients of RAG Applications.
Participants should have a basic understanding of Python programming and a foundational knowledge of Large Language Models (LLMs) to make the most of this course.
By the end of this course, you'll be able to develop RAG applications using Large Language Models, LangChain, and Vector Databases. You will learn to write effective prompts, understand models and tokens, and apply vector databases to automate workflows. You'll also grasp key LangChain concepts to build simple to medium complexity RAG applications.
Status: LangChain
LangChain
Status: Retrieval-Augmented Generation
Retrieval-Augmented Generation
Beginner·Course·3 hours
Featured reviews
5.0
·Reviewed Jul 17, 2026
It was wonderful program to enhance your upskill with RAG
5.0
·Reviewed Aug 8, 2025
The Coure content gives a Comprahensive understandung of RAG
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Д
Дмитро
5.0
·Reviewed Jul 23, 2025
This course provided a clear and structured introduction to Retrieval-Augmented Generation and its practical applications. I appreciated the balance between theory and hands-on projects, especially the use of LangChain and FAISS for building real-world solutions like document summarization and chatbots. The step-by-step explanations helped me understand how to integrate vector databases, prompt templates, and LLMs into functional systems. I now feel confident to start developing my own RAG-based applications and explore more advanced use cases. A great starting point for anyone interested in applied AI and automation.
蕭
蕭百堅
3.0
·Reviewed Jan 21, 2025
I cannot find any code snippets.
R
Ravi
5.0
·Reviewed Aug 9, 2025
The Coure content gives a Comprahensive understandung of RAG
R
Ritesh
5.0
·Reviewed Jul 18, 2026
It was wonderful program to enhance your upskill with RAG
J
Jacob
5.0
·Reviewed May 21, 2025
Excellent. Well done. Short and Sweet -- and practical.
Y
Yogesh
5.0
·Reviewed Jul 8, 2026
I am Proud of you to give me certificate
J
Jagapathy
5.0
·Reviewed Jul 2, 2026
Great learning experience
M
Milind
5.0
·Reviewed Sep 25, 2025
good course
I
Ismail
5.0
·Reviewed Feb 2, 2025
interesting
B
Braian
5.0
·Reviewed Jan 6, 2025
excelente!
N
Natasha
5.0
·Reviewed Feb 19, 2026
very good
H
Hana
4.0
·Reviewed Dec 29, 2024
The course provides a clear and accessible introduction to RAG, explaining how retrieval systems and large language models (LLMs) work together to generate more accurate and contextually relevant outputs.
What I loved most was the balance of theory and hands-on practice.
The two real-world applications projects such as Invoice Parsing and The HR Policy ChatBot, were very engaging and helped contextualize the theoretical concepts. The instructor’s clear explanations and beginner-friendly approach made it easy to follow along, even with more complex topics.
This course is a great starting point for anyone looking to understand and work with Retrieval-Augmented Generation systems. It’s informative, practical, and offers a strong foundation for further exploration in the field.
N
Natthanicha
4.0
·Reviewed Apr 24, 2026
It's great, but I'd like you to provide a tutorial from the beginning, including installing the essential libraries and which files to create. That would be really helpful.
