Building RAG Applications

  • BeginnerLevel

  • 133+Students Enrolled

  • 2 Hrs Duration

  • 4.8Average Rating

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About this Course

  • This course introduces the core ideas behind Retrieval Augmented Generation and explains how RAG systems combine large language models with external knowledge sources.
  • Learn the complete RAG pipeline including document loaders, chunking strategies, embedding models, vector databases, and retrieval mechanisms used in modern AI systems.
  • Explore how to evaluate RAG systems using metrics and frameworks such as RAGAS and DeepEval to measure retrieval and generation performance.
  • Build a practical Company Policy RAG system that demonstrates how RAG applications answer questions from internal documents and enterprise data.

Course Benefits

  • Gain a clear understanding of how Retrieval Augmented Generation systems combine LLMs with external data sources to improve accuracy and reliability.
  • Learn the full architecture of modern RAG pipelines including document ingestion, embeddings, vector databases, and retrieval strategies.
  • Understand how vector search and embeddings enable semantic retrieval from large document collections.
  • Explore modern RAG evaluation techniques and frameworks used to measure retrieval and generation quality.
  • Build a practical RAG application that demonstrates how organizations deploy AI assistants on internal knowledge bases.

Learning Outcomes

Understand RAG Systems

Understand RAG architecture and retrieval pipelines.

Build Retrieval Pipelines

Learn document ingestion, embeddings, and vector search.

Evaluate RAG Applications

Use evaluation metrics and frameworks for RAG systems.

Who Should Enroll

  • AI engineers and developers who want to understand RAG systems and build retrieval-based AI applications.
  • Data scientists interested in learning how large LLM integrate with external data sources using a retrieval pipeline.
  • Machine learning practitioners exploring vector databases, embeddings, & evaluation techniques for modern GenAI systems.
  • Students and professionals wanting to understand the foundations of RAG and enterprise RAG applications.

Course Curriculum

This curriculum explains the complete RAG pipeline from fundamentals to hands-on implementation. Learn retrieval workflows, document processing, embedding models, vector databases, retrievers, evaluation metrics, and build a practical RAG system.

tools

Learn the motivation behind RAG systems and how they extend large language models with external knowledge sources. Understand RAG architecture, key components, and how retrieval improves LLM accuracy and reliability.

  1. 1. Course Introduction

  2. 2. Why RAG Systems?

  3. 3. What is RAG System?

Understand the complete retrieval pipeline used in RAG systems including document ingestion, chunking strategies, embedding generation, vector database indexing, retrieval mechanisms, and evaluation frameworks for RAG performance.

  1. 1. Introduction to Retrieval

  2. 2. Document Loaders

  3. 3. Document splitters and chunkers

  4. 4. Embedding Models

  5. 5. Vector Databases

  6. 6. Retrievers

  7. 7. Introduction to RAG Evaluation Matrix

  8. 8. Popular RAG Evaluation Frameworks

Build a practical RAG system that answers questions from company policy documents. Learn how retrieval pipelines interact with language models to generate accurate responses from enterprise knowledge sources.

  1. 1. Company Policy RAG

Meet the instructor

Our instructor and mentors carry years of experience in data industry

company logo
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.

Get this Course Now

With this course you’ll get

  • 2 Hours

    Duration

  • Dipanjan Sarkar

    Instructor

  • Beginner

    Level

Certificate of completion

Earn a professional certificate upon course completion

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

Frequently Asked Questions

Looking for answers to other questions?

A RAG system combines a large language model with a retrieval system that fetches relevant information from external data sources. Instead of relying only on training data, the model retrieves documents from a knowledge base and uses them to generate accurate responses.

RAG systems help solve key limitations of large language models such as hallucination and outdated knowledge. By retrieving information from external sources like company documents or databases, they ensure that responses are grounded in real data.

A typical RAG pipeline includes document loaders, document splitters, embedding models, vector databases, and retrievers. These components work together to convert documents into searchable vectors and retrieve the most relevant information.

Document loaders are tools used to ingest data from various sources such as PDFs, web pages, or databases. They convert raw data into structured documents that can be processed and indexed for retrieval.

Large documents are split into smaller chunks so that embedding models can represent them effectively. Chunking ensures that retrieval systems return precise and relevant information instead of large irrelevant text blocks.

Embeddings convert text into numerical vectors that capture semantic meaning. These vectors allow AI systems to perform similarity search and retrieve documents that are most relevant to a user's query.

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