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RAG Systems in Practice

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RAG Systems in Practice

Instructor: Edureka

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Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • How to build and optimize Retrieval-Augmented Generation (RAG) systems using LangChain and FAISS.

  • Techniques for enhancing retrieval accuracy through hybrid search, re-ranking, and grounding methods.

  • How to deploy RAG systems into production environments and integrate them with APIs and platforms like Streamlit.

  • Best practices for monitoring, evaluating, and scaling RAG systems for optimal performance.

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Recently updated!

January 2026

Assessments

14 assignments

Taught in English

Build your subject-matter expertise

This course is part of the LLM Engineering: Prompting, Fine-Tuning, Optimization & RAG Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There are 4 modules in this course

This course introduces the core concepts and techniques behind Retrieval-Augmented Generation (RAG) systems, guiding you through building, optimizing, and deploying powerful AI systems that combine language models with external knowledge sources. Whether you are new to RAG or looking to deepen your understanding, this course provides a hands-on approach to mastering RAG workflows and improving model accuracy.

Through detailed lessons, demonstrations, and real-world applications, you’ll learn how to preprocess and index documents, generate embeddings, construct RAG pipelines, and deploy production-ready systems. You’ll also explore advanced optimization techniques to enhance retrieval quality, scalability, and context relevance. By the end of this course, you will be able to: • Understand the fundamentals of Retrieval-Augmented Generation and its applications in AI. • Apply text preprocessing and embedding techniques to improve document retrieval. • Build and optimize RAG pipelines using LangChain and FAISS. • Utilize hybrid retrieval, re-ranking, and grounding methods to enhance context accuracy. • Deploy and evaluate RAG systems in production environments for optimal performance. This course is ideal for AI enthusiasts, machine learning practitioners, and developers looking to specialize in building advanced AI systems that integrate external knowledge with language models. No prior experience with RAG systems is required, but a basic understanding of Python and machine learning concepts will be beneficial. Join us to begin your journey into the world of Retrieval-Augmented Generation and learn how to build efficient, scalable, and accurate AI systems!

In this module, learners will explore the fundamentals of Retrieval-Augmented Generation (RAG), including how it combines language models with external knowledge sources for improved accuracy. Key concepts such as text embeddings, vector stores, and document preprocessing will be introduced, with hands-on demonstrations to build simple RAG workflows and visualize context retrieval.

What's included

13 videos5 readings4 assignments1 discussion prompt

13 videos•Total 72 minutes
  • Specialization Introduction•7 minutes
  • Course Introduction•4 minutes
  • Introduction to RAG•7 minutes
  • Demonstration: Building Simple Rag Workflow•6 minutes
  • Demonstration: Visualizing Context Retrieval Flow-I•5 minutes
  • Demonstration: Visualizing Context Retrieval Flow II•5 minutes
  • Importance of Embeddings in Retrieval System Design•5 minutes
  • Understanding Text Embeddings and Similarity Search•5 minutes
  • Demonstration: Generating Embeddings Using OpenAI API•6 minutes
  • Demonstration: Building a FAISS Vector Store•5 minutes
  • Splitting and Cleaning Documents for Indexing•5 minutes
  • Demonstration: Using LangChain Loaders for PDFs and Text Files•6 minutes
  • Demonstration: Chunking and Normalizing Text Data•6 minutes
5 readings•Total 85 minutes
  • Welcome to RAG Systems in Practice•10 minutes
  • Overview of Retrieval-Augmented Generation Systems•20 minutes
  • Text Embeddings and Semantic Search Fundamentals•20 minutes
  • Document Preprocessing Techniques for RAG Systems•20 minutes
  • Module Summary: Introduction to Retrieval Systems•15 minutes
4 assignments•Total 48 minutes
  • Knowledge Check: Introduction to Retrieval Systems•30 minutes
  • Practice Knowledge Check: Understanding Retrieval-Augmented Generation (RAG)•6 minutes
  • Practice Knowledge Check: Embeddings and Vector Stores•6 minutes
  • Practice Knowledge Check: Preprocessing for Effective Retrieval•6 minutes
1 discussion prompt•Total 10 minutes
  • Introduce Yourself•10 minutes

Learners will focus on building and optimizing RAG pipelines using LangChain. They will explore techniques like hybrid retrieval, re-ranking, and grounding to improve context accuracy. The module includes practical applications for creating, testing, and evaluating high-performance RAG workflows.

What's included

16 videos5 readings5 assignments

16 videos•Total 96 minutes
  • Retrieval Pipelines in RAG Systems•6 minutes
  • Connecting Vector Stores to LLMs•6 minutes
  • Demonstration: Creating a Retriever Chain with LangChain•4 minutes
  • Demonstration: Query Testing and Context Ranking•7 minutes
  • Hybrid Retrieval and Re-Ranking in RAG•6 minutes
  • Re-Ranking with Cross-Encoder and BM25•7 minutes
  • Demonstration: Combining Dense and Sparse Retrieval•6 minutes
  • Demonstration: Evaluating Retrieval Precision•6 minutes
  • Hallucinations as a Retrieval Problem•6 minutes
  • Context Window Management•5 minutes
  • Demonstration: Reducing Hallucinations via Grounded Context•7 minutes
  • Demonstration: Adding Citation References in RAG Output•6 minutes
  • Introduction to LangGraph•5 minutes
  • Demonstration: Building a Stateful RAG Graph with LangGraph•7 minutes
  • Demonstration: Decision-Driven RAG Orchestration with LangGraph - I•6 minutes
  • Demonstration: Decision-Driven RAG Orchestration with LangGraph - II•6 minutes
5 readings•Total 120 minutes
  • Building Retrieval Pipelines with LangChain and FAISS•45 minutes
  • Hybrid Search Techniques for Context Accuracy•20 minutes
  • Improving Context Relevance and Grounding in RAG•20 minutes
  • Designing Graph-Based LLM Workflows with LangGraph•20 minutes
  • Module Summary : Building and Optimizing RAG Pipelines•15 minutes
5 assignments•Total 54 minutes
  • Knowledge Check: Building and Optimizing RAG Pipelines•30 minutes
  • Practice Knowledge Check: Retrieval Pipelines in LangChain•6 minutes
  • Practice Knowledge Check: Hybrid and Re-Ranking Techniques•6 minutes
  • Practice Knowledge Check: Enhancing Context Quality•6 minutes
  • Practice Knowledge Check: Orchestrating RAG Workflows with LangGraph•6 minutes

This module covers the deployment and evaluation of RAG systems in real-world applications. Learners will explore deployment strategies, API integration, and performance monitoring. They will also learn how to optimize RAG systems for scalability and efficiency in production environments.

What's included

19 videos5 readings4 assignments

19 videos•Total 100 minutes
  • RAG System Deployment in Production•5 minutes
  • Optimized End-to-End RAG Pipeline and System Design•6 minutes
  • Demonstration: Deploying RAG App with Streamlit : RAG Core - Retrieval Setup•7 minutes
  • Demonstration: Deploying RAG App with Streamlit : RAG Core - Question Answering•3 minutes
  • Demonstration: Deploying RAG App with Streamlit : Data and Authentication•6 minutes
  • Demonstration: Deploying RAG App with Streamlit : Ingestion and Prompt Design•5 minutes
  • Demonstration: Deploying RAG App with Streamlit : Context Retrieval and Utils•4 minutes
  • Demonstration: Deploying RAG App with Streamlit : Deployment •5 minutes
  • Demonstration: Integrating API Endpoints for Retrieval-I•7 minutes
  • Demonstration: Integrating API Endpoints for Retrieval-II•6 minutes
  • Evaluating RAG System Performance•5 minutes
  • Benchmarking RAG Performance•3 minutes
  • Demonstration: Using LangSmith or LlamaIndex for Local Evaluation•6 minutes
  • Demonstration: Analyzing Cost and Latency Metrics•6 minutes
  • Accuracy vs Scalability in RAG•4 minutes
  • Enhancing Query Understanding and Ranking•4 minutes
  • Demonstration: Implementing Hybrid Retrieval at Scale•6 minutes
  • Demonstration: Optimizing Latency and Throughput for RAG Systems•6 minutes
  • Production-Ready RAG System Architecture•5 minutes
5 readings•Total 105 minutes
  • Deploying Retrieval-Augmented Generation Applications•20 minutes
  • Evaluating RAG Pipelines: Metrics and Observability Tools•20 minutes
  • Scaling RAG Systems for High-Performance Applications•20 minutes
  • Module Summary : Deploying and Evaluating RAG Systems•15 minutes
  • A Practical Guide to Building Scalable LLM Applications•30 minutes
4 assignments•Total 48 minutes
  • Knowledge Check: Deploying and Evaluating RAG Systems•30 minutes
  • Practice Knowledge Check: RAG Deployment Fundamentals•6 minutes
  • Practice Knowledge Check: Monitoring and Evaluation•6 minutes
  • Practice Knowledge Check: Retrieval Accuracy and Scalability•6 minutes

In the final module, learners will apply their knowledge by completing a practice project and final assessment. They will review key concepts and build a production-ready RAG system, preparing them to implement RAG in real-world projects.

What's included

1 video1 reading1 assignment1 discussion prompt

1 video•Total 2 minutes
  • Course Summary: RAG Systems in Practice•2 minutes
1 reading•Total 45 minutes
  • Practice Project: Building and Deploying a Scalable RAG System•45 minutes
1 assignment•Total 30 minutes
  • End Course Knowledge Check: RAG Systems in Practice•30 minutes
1 discussion prompt•Total 10 minutes
  • Describe your Learning Journey•10 minutes

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Instructor

Edureka
203 Courses•185,724 learners

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Frequently asked questions

This course teaches how to build, optimize, and deploy Retrieval-Augmented Generation (RAG) systems, integrating language models with external knowledge sources for more accurate AI responses.

This course is for AI enthusiasts, machine learning practitioners, and developers interested in learning how to build advanced retrieval-based AI systems.

A basic understanding of Python and machine learning concepts is recommended for this course, though no prior RAG experience is required.

You will use LangChain, FAISS, Streamlit, and APIs, among other tools, to build and deploy RAG systems.

You will learn how to preprocess documents, build retrieval pipelines, optimize RAG systems, and deploy them for real-world applications.

Yes, this course is beginner-friendly, but some basic understanding of machine learning and Python will help you follow along more effectively.

You will gain hands-on experience with building RAG workflows, optimizing context accuracy, and deploying RAG systems into production environments.

The course consists of four modules, each focusing on different aspects of RAG systems, from foundational concepts to advanced deployment and optimization.

Yes, there are practice assignments after each module to help reinforce your learning and a final project to apply all the concepts.

By the end of the course, you will be able to design, implement, and deploy production-ready RAG systems and apply these skills to real-world AI applications.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Financial aid available,