What tools and technologies will I use?

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

What will I learn in this course?

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

Can I take this course if I’m a beginner in AI?

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

What practical skills will I gain from this course?

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

How is this course structured?

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

Are there any assignments in the course?

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

What are the benefits of completing this course?

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.

What will I get if I subscribe to this Specialization?

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.

Is financial aid available?

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.

RAG Systems in Practice

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Ce cours n'est pas disponible en Français (France)

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RAG Systems in Practice

Ce cours fait partie de Spécialisation "LLM Engineering: Prompting, Fine-Tuning, Optimization & RAG"

Instructeur : Edureka

Inclus avec

4 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Débutant

Expérience recommandée

1 semaine à compléter

à 10 heures par semaine

Planning flexible

Apprenez à votre propre rythme

4 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Débutant

Expérience recommandée

1 semaine à compléter

à 10 heures par semaine

Planning flexible

Apprenez à votre propre rythme

Ce que vous apprendrez

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.

Compétences que vous acquerrez

Catégorie : Retrieval-Augmented Generation
Catégorie : Large Language Modeling
Catégorie : Data Preprocessing
Catégorie : Model Evaluation
Catégorie : Embeddings
Catégorie : Scalability
Catégorie : Performance Tuning

Outils que vous découvrirez

Catégorie : Vector Databases
Catégorie : Prompt Engineering
Catégorie : Model Deployment
Catégorie : LangGraph
Catégorie : AI Workflows
Catégorie : Generative AI
Catégorie : LangChain

Détails à connaître

Certificat partageable

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Récemment mis à jour !

janvier 2026

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Ce cours fait partie de la Spécialisation "LLM Engineering: Prompting, Fine-Tuning, Optimization & RAG"

Lorsque vous vous inscrivez à ce cours, vous êtes également inscrit(e) à cette Spécialisation.

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Il y a 4 modules dans ce cours

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.

Inclus

13 vidéos5 lectures4 devoirs1 sujet de discussion

13 vidéos 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 lectures 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 devoirs Total 48 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
Knowledge Check: Introduction to Retrieval Systems 30 minutes

1 sujet de discussion 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.

Inclus

16 vidéos5 lectures5 devoirs

16 vidéos 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 lectures 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 devoirs Total 54 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
Knowledge Check: Building and Optimizing RAG Pipelines 30 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.

Inclus

19 vidéos5 lectures4 devoirs

19 vidéos 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 lectures 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 devoirs Total 48 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
Knowledge Check: Deploying and Evaluating RAG Systems 30 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.

Inclus

1 vidéo1 lecture1 devoir1 sujet de discussion

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Instructeur

Edureka

159 Cours 148 606 apprenants

Offert par

Edureka

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Foire Aux 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.

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.