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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RAG Systems in Practice

Dieser Kurs ist Teil von Spezialisierung für LLM Engineering: Prompting, Fine-Tuning, Optimization & RAG

Dozent: Edureka

Bei enthalten

Mehr erfahren

4 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

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Empfohlene Erfahrung

1 Woche zu vervollständigen

unter 10 Stunden pro Woche

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

4 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

Stufe Anfänger

Empfohlene Erfahrung

1 Woche zu vervollständigen

unter 10 Stunden pro Woche

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

Was Sie lernen werden

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.

Kompetenzen, die Sie erwerben

Kategorie: Model Deployment
Kategorie: LangGraph
Kategorie: Retrieval-Augmented Generation
Kategorie: Data Preprocessing
Kategorie: Large Language Modeling
Kategorie: Vector Databases
Kategorie: Performance Tuning
Kategorie: Scalability
Kategorie: Embeddings
Kategorie: Prompt Engineering
Kategorie: LangChain
Kategorie: AI Workflows
Kategorie: Generative AI
Kategorie: Model Evaluation

Wichtige Details

Zertifikat zur Vorlage

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Januar 2026

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14 Aufgaben

Unterrichtet in Englisch

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Dieser Kurs ist Teil der Spezialisierung Spezialisierung für LLM Engineering: Prompting, Fine-Tuning, Optimization & RAG

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In diesem Kurs gibt es 4 Module

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.

Das ist alles enthalten

13 Videos5 Lektüren4 Aufgaben1 Diskussionsthema

13 Videos Insgesamt 72 Minuten

Specialization Introduction 7 Minuten
Course Introduction 4 Minuten
Introduction to RAG 7 Minuten
Demonstration: Building Simple Rag Workflow 6 Minuten
Demonstration: Visualizing Context Retrieval Flow-I 5 Minuten
Demonstration: Visualizing Context Retrieval Flow II 5 Minuten
Importance of Embeddings in Retrieval System Design 5 Minuten
Understanding Text Embeddings and Similarity Search 5 Minuten
Demonstration: Generating Embeddings Using OpenAI API 6 Minuten
Demonstration: Building a FAISS Vector Store 5 Minuten
Splitting and Cleaning Documents for Indexing 5 Minuten
Demonstration: Using LangChain Loaders for PDFs and Text Files 6 Minuten
Demonstration: Chunking and Normalizing Text Data 6 Minuten

5 Lektüren Insgesamt 85 Minuten

Welcome to RAG Systems in Practice 10 Minuten
Overview of Retrieval-Augmented Generation Systems 20 Minuten
Text Embeddings and Semantic Search Fundamentals 20 Minuten
Document Preprocessing Techniques for RAG Systems 20 Minuten
Module Summary: Introduction to Retrieval Systems 15 Minuten

4 Aufgaben Insgesamt 48 Minuten

Practice Knowledge Check: Understanding Retrieval-Augmented Generation (RAG) 6 Minuten
Practice Knowledge Check: Embeddings and Vector Stores 6 Minuten
Practice Knowledge Check: Preprocessing for Effective Retrieval 6 Minuten
Knowledge Check: Introduction to Retrieval Systems 30 Minuten

1 Diskussionsthema Insgesamt 10 Minuten

Introduce Yourself 10 Minuten

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.

Das ist alles enthalten

16 Videos5 Lektüren5 Aufgaben

16 Videos Insgesamt 96 Minuten

Retrieval Pipelines in RAG Systems 6 Minuten
Connecting Vector Stores to LLMs 6 Minuten
Demonstration: Creating a Retriever Chain with LangChain 4 Minuten
Demonstration: Query Testing and Context Ranking 7 Minuten
Hybrid Retrieval and Re-Ranking in RAG 6 Minuten
Re-Ranking with Cross-Encoder and BM25 7 Minuten
Demonstration: Combining Dense and Sparse Retrieval 6 Minuten
Demonstration: Evaluating Retrieval Precision 6 Minuten
Hallucinations as a Retrieval Problem 6 Minuten
Context Window Management 5 Minuten
Demonstration: Reducing Hallucinations via Grounded Context 7 Minuten
Demonstration: Adding Citation References in RAG Output 6 Minuten
Introduction to LangGraph 5 Minuten
Demonstration: Building a Stateful RAG Graph with LangGraph 7 Minuten
Demonstration: Decision-Driven RAG Orchestration with LangGraph - I 6 Minuten
Demonstration: Decision-Driven RAG Orchestration with LangGraph - II 6 Minuten

5 Lektüren Insgesamt 120 Minuten

Building Retrieval Pipelines with LangChain and FAISS 45 Minuten
Hybrid Search Techniques for Context Accuracy 20 Minuten
Improving Context Relevance and Grounding in RAG 20 Minuten
Designing Graph-Based LLM Workflows with LangGraph 20 Minuten
Module Summary : Building and Optimizing RAG Pipelines 15 Minuten

5 Aufgaben Insgesamt 54 Minuten

Practice Knowledge Check: Retrieval Pipelines in LangChain 6 Minuten
Practice Knowledge Check: Hybrid and Re-Ranking Techniques 6 Minuten
Practice Knowledge Check: Enhancing Context Quality 6 Minuten
Practice Knowledge Check: Orchestrating RAG Workflows with LangGraph 6 Minuten
Knowledge Check: Building and Optimizing RAG Pipelines 30 Minuten

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.

Das ist alles enthalten

19 Videos5 Lektüren4 Aufgaben

19 Videos Insgesamt 100 Minuten

RAG System Deployment in Production 5 Minuten
Optimized End-to-End RAG Pipeline and System Design 6 Minuten
Demonstration: Deploying RAG App with Streamlit : RAG Core - Retrieval Setup 7 Minuten
Demonstration: Deploying RAG App with Streamlit : RAG Core - Question Answering 3 Minuten
Demonstration: Deploying RAG App with Streamlit : Data and Authentication 6 Minuten
Demonstration: Deploying RAG App with Streamlit : Ingestion and Prompt Design 5 Minuten
Demonstration: Deploying RAG App with Streamlit : Context Retrieval and Utils 4 Minuten
Demonstration: Deploying RAG App with Streamlit : Deployment 5 Minuten
Demonstration: Integrating API Endpoints for Retrieval-I 7 Minuten
Demonstration: Integrating API Endpoints for Retrieval-II 6 Minuten
Evaluating RAG System Performance 5 Minuten
Benchmarking RAG Performance 3 Minuten
Demonstration: Using LangSmith or LlamaIndex for Local Evaluation 6 Minuten
Demonstration: Analyzing Cost and Latency Metrics 6 Minuten
Accuracy vs Scalability in RAG 4 Minuten
Enhancing Query Understanding and Ranking 4 Minuten
Demonstration: Implementing Hybrid Retrieval at Scale 6 Minuten
Demonstration: Optimizing Latency and Throughput for RAG Systems 6 Minuten
Production-Ready RAG System Architecture 5 Minuten

5 Lektüren Insgesamt 105 Minuten

Deploying Retrieval-Augmented Generation Applications 20 Minuten
Evaluating RAG Pipelines: Metrics and Observability Tools 20 Minuten
Scaling RAG Systems for High-Performance Applications 20 Minuten
Module Summary : Deploying and Evaluating RAG Systems 15 Minuten
A Practical Guide to Building Scalable LLM Applications 30 Minuten

4 Aufgaben Insgesamt 48 Minuten

Practice Knowledge Check: RAG Deployment Fundamentals 6 Minuten
Practice Knowledge Check: Monitoring and Evaluation 6 Minuten
Practice Knowledge Check: Retrieval Accuracy and Scalability 6 Minuten
Knowledge Check: Deploying and Evaluating RAG Systems 30 Minuten

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.

Das ist alles enthalten

1 Video1 Lektüre1 Aufgabe1 Diskussionsthema

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Häufig gestellte Fragen

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.