Building and Evaluating RAG System

  • IntermediateLevel

  • 694+Students Enrolled

  • 2 Hrs Duration

  • 4.6Average Rating

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About this Course

  • Learn to build RAG system applications that combine large language models with retrieval-based intelligence for real-world problem solving.
  • Explore the architecture, components, and workflow of a RAG system, from data retrieval to contextual response generation.
  • Understand how to build AI agents powered by RAG that deliver factual, explainable, and reliable outputs.
  • Work on practical use cases like document search, chatbot development, and automated question answering.
  • Follow this retrieval augmented generation course to learn the end-to-end process of building and evaluating RAG pipelines.
  • Implement strategies for evaluation, optimization, and latency reduction in RAG-based systems.

Learning Outcomes

Master RAG fundamentals

Understand the building blocks of retrieval-augmented generation.

Build AI Agents

Learn to build RAG-powered AI agents using context retrieval and GenAI

Evaluation & Optimization

Evaluate RAG, improve retrieval accuracy, reduce hallucination.

Who Should Enroll

  • Developers and AI practitioners looking to build RAG applications with practical workflows.
  • Data scientists interested in understanding retrieval-augmented generation models.
  • Learners who want to build AI agents powered by RAG for real-world problem solving.
  • Students or professionals taking a RAG system course online to upskill in GenAI.

Course Curriculum

This retrieval-augmented generation course helps you learn to build RAG system applications from end to end. Understand embeddings, vector databases, context retrieval, and evaluation techniques while deploying RAG-powered AI agents.

tools

  1. 1. Introduction to RAG

  2. 2. RAG-based Q&A system pipeline

  3. 3. Pain Points in RAG system

  4. 4. Evaluation Measured: Retrieval Metrics

  1. 1. Advanced RAG Techniques and Colpali RAG

  2. 2. Hands On: Colpali RAG

  3. 3. LangGraph Studio: Building,Tracing & Evaluating AI Agents

  4. 4. Hands On: GPT 4o Research Agent in LangGraph

Meet the instructor

Our instructor and mentors carry years of experience in data industry

company logo
Nikhil Pentapalli

Senior ML Engineer - Adobe

Nikhil is a Senior Machine Learning Engineer at Adobe, specializing in LLMs, NLP, RAG, and document intelligence. He builds AI-powered Q&A systems and generative AI evaluation metrics.

Get this Course Now

With this course you’ll get

  • 2 Hours

    Duration

  • Nikhil Pentapalli

    Instructor

  • Intermediate

    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, or retrieval-augmented generation system, combines a language model with data retrieval. It improves accuracy by using external information instead of relying only on stored knowledge. This approach helps reduce hallucinations and produces more reliable and factual AI-generated responses.

You will learn how to build RAG system applications and connect large language models with retrievers. The course covers data retrieval, integration, evaluation, and deployment. By the end, you’ll create production-ready RAG-powered AI solutions efficiently.

Basic knowledge of Python and AI is helpful but not required. The course explains every concept step by step. You’ll learn tools, techniques, and code implementations easily. It is designed for learners of all experience levels.

Retrieval-augmented generation combines information retrieval and generative AI. It enables models to produce responses using real data retrieved from external sources. This method ensures more factual, transparent, and contextually accurate outputs than traditional generative models.

RAG-powered AI agents generate accurate, context-aware, and transparent responses. They use both retrieval and generation for better understanding. This makes them ideal for chatbots, search tools, and enterprise applications where reliability and precision are essential.

You will work with vector databases, embedding models, and frameworks like LangChain and FAISS. These tools help you build, retrieve, and evaluate data efficiently to create fully functional RAG systems.

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