Real World Projects on RAG

  • IntermediateLevel

  • 147+Students Enrolled

  • 5 Hrs Duration

  • 4.6Average Rating

About this Course

  • Build your first RAG system using LlamaIndex, covering document ingestion, chunking, embeddings, retrieval pipelines, and response generation workflows.
  • Create a question answering RAG application with LangChain that retrieves relevant context from documents and generates accurate, grounded answers.
  • Develop an advanced RAG based query resolution system that improves response quality by combining retrieval strategies with structured reasoning flows.
  • Learn how to design scalable RAG applications suitable for industry use cases, focusing on performance, modular pipelines, and large document collections.
  • Build an end to end RAG application using LangChain and Streamlit, integrating backend retrieval logic with a clean, interactive user interface.

Learning Outcomes

RAG with LlamaIndex

Build ingestion, indexing, and retrieval pipelines using LlamaIndex

QA RAG using LangChain

Create document grounded question answering RAG applications

Scalable RAG Pipelines

Build RAG systems that scale across large document datasets

Who Should Enroll

  • Data professionals and developers who want hands on experience building real world RAG applications using LangChain.
  • ML & GenAI practitioners looking to strengthen their portfolio with end-to-end, project-based RAG implementation.
  • Professionals aiming to apply retrieval augmented generation to real business problems through industry projects.

Course Curriculum

Learn RAG fundamentals through hands-on projects covering document ingestion, embeddings, retrieval pipelines, QA systems, scalable architectures, and end-to-end RAG app development with UI integration.

toolstoolstools

  1. 1. Introduction to RAG systems

  2. 2. Getting Started with LlamaIndex

  3. 3. Components of LlamaIndex

  4. 4. Build Your First RAG system using LlamaIndex

  1. 1. Hands On: Build a QA RAG System with Langchain

  2. 2. Course Handouts

  1. 1. Learn RAG based system

  2. 2. Hands-on: RAG based system

  3. 3. Simplified RAG Architecture

  1. 1. Hands On: Building Scalable Industry Applications with RAG and Agentic Systems

  1. 1. Hands On: End to End RAG Application Development

Meet the instructor

Our instructor and mentors carry years of experience in data industry

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Anuj Saini

AI Research Scholar, Montréal

Anuj Saini, a Subject Matter Expert, has 15+ years of experience in NLP, Search, Data Science, ML, and Cloud. He excels in building AI-driven systems. Connect on LinkedIn.

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Dipanjan Sarkar

Principal AI Scientist

Dipanjan Sarkar, Head of AI, Author & Consultant, has 12+ years of expertise in ML, DL, GenAI, CV & NLP. He has led AI initiatives across Fortune 100 firms & startups, building data products & upskilling professionals at all levels.

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Apoorv Vishnoi

Head - Training, Analytics Vidhya

Apoorv is a seasoned AI professional with over 14 years of experience. He has founded companies, worked at start-ups and mentored start-ups at incubation cells.

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Prashant Sahu

Manager, Analytics Vidhya

Prashant Sahu, Ph.D IIT Bombay; Data Science Manager, Analytics Vidhya A dynamic and innovative Data Scientist who brings extensive experience in Artificial Intelligence, Machine Learning, and Advanced Analytics to the table.

Get this Course Now

With this course you’ll get

  • 5 Hours

    Duration

  • Intermediate

    Level

Certificate of completion

Complete the course and pass the final assessment to receive an industry-recognized certificate

  • Industry-Recognized Credential
  • Career Advancement Credential
  • Shareable Achievement
certificate

Frequently Asked Questions

Looking for answers to other questions?

Retrieval Augmented Generation is a technique that enhances language model responses by retrieving relevant information from external data sources, ensuring outputs are grounded in factual, up to date, and domain specific knowledge.

RAG reduces hallucinations and improves accuracy by combining retrieval with generation, making it suitable for applications like question answering, knowledge assistants, and enterprise search systems that rely on trusted information.

A typical RAG pipeline includes document ingestion, chunking, embedding, vector storage, retrieval of relevant context, and response generation using a language model grounded in retrieved information.

By retrieving relevant source content before generation, RAG ensures responses are based on factual context, improving transparency, trustworthiness, and consistency compared to standalone language model outputs.

The QA project mirrors real world knowledge assistant scenarios, where user questions must be answered accurately by retrieving relevant context from documents rather than relying on language model memory alone.

The end to end project demonstrates how backend RAG pipelines can be exposed through a user interface, helping learners understand how RAG systems are delivered as usable applications.

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