К содержимому
learnspaceYOUR NEXT CHAPTER
ПРОСТРАНСТВО ОБУЧЕНИЯ
ГлавнаяКаталог курсовМоё обучениеCoursera

Знания без границ

Учитесь у лучших университетов и компаний мира.

Открыть Coursera
Интеграция
Пространство университета
Моё пространствоСтраница курса
↵
ЯЛичный кабинетСтудент
© 2026 LearnSpaceКаждый день — возможность узнать больше.Помощь
RAG Systems in Practice · LearnSpace
Назад в каталог
courseraПрограммирование

RAG Systems in Practice

Курс от Edureka
Начальный≈ 13.8 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

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!

Навыки, которые вы освоите

Retrieval-Augmented GenerationEmbeddingsData PreprocessingVector DatabasesScalabilityModel EvaluationPerformance TestingContinuous MonitoringLarge Language ModelingAI WorkflowsPerformance TuningGenerative AIApplication DeploymentContext ManagementAI IntegrationsModel DeploymentLangChainLLM ApplicationLangGraph

Программа курса

4 модулей · 81 учебных материалов

01Introduction to Retrieval Systems23 материалов

Understanding Retrieval-Augmented Generation (RAG)

Specialization IntroductionВидеоCourse IntroductionВидеоWelcome to RAG Systems in PracticeЧтениеIntroduction to RAGВидео

Учитесь у экспертов

Edureka

Преподаватель курса

RAG Systems in Practice
В каталоге вашей программы

Инвестируйте в себя

Новые знания — в удобное для вас время.

Начать на Coursera

Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 13.8 ч

4 модулей

Язык: Английский

Субтитры: Арабский, Французский, Итальянский, Бразильский португальский, Корейский, Немецкий, Испанский, Японский

Часть программы вашего университета
Demonstration: Building Simple Rag WorkflowВидео
Demonstration: Visualizing Context Retrieval Flow-IВидео
Demonstration: Visualizing Context Retrieval Flow IIВидео
Introduce YourselfОбсуждение
Overview of Retrieval-Augmented Generation SystemsЧтение
Practice Knowledge Check: Understanding Retrieval-Augmented Generation (RAG)Задание

Embeddings and Vector Stores

Importance of Embeddings in Retrieval System DesignВидеоUnderstanding Text Embeddings and Similarity SearchВидеоDemonstration: Generating Embeddings Using OpenAI APIВидеоDemonstration: Building a FAISS Vector StoreВидеоText Embeddings and Semantic Search FundamentalsЧтениеPractice Knowledge Check: Embeddings and Vector StoresЗадание

Preprocessing for Effective Retrieval

Splitting and Cleaning Documents for IndexingВидеоDemonstration: Using LangChain Loaders for PDFs and Text FilesВидеоDemonstration: Chunking and Normalizing Text DataВидеоDocument Preprocessing Techniques for RAG SystemsЧтениеPractice Knowledge Check: Preprocessing for Effective RetrievalЗадание

Module Wrap-Up and Assessment

Module Summary: Introduction to Retrieval SystemsЧтениеKnowledge Check: Introduction to Retrieval SystemsЗадание
02Building and Optimizing RAG Pipelines26 материалов

Retrieval Pipelines in LangChain

Retrieval Pipelines in RAG SystemsВидеоConnecting Vector Stores to LLMsВидеоDemonstration: Creating a Retriever Chain with LangChainВидеоDemonstration: Query Testing and Context RankingВидеоBuilding Retrieval Pipelines with LangChain and FAISSЧтениеPractice Knowledge Check: Retrieval Pipelines in LangChainЗадание

Hybrid and Re-Ranking Techniques

Hybrid Retrieval and Re-Ranking in RAGВидеоRe-Ranking with Cross-Encoder and BM25ВидеоDemonstration: Combining Dense and Sparse RetrievalВидеоDemonstration: Evaluating Retrieval PrecisionВидеоHybrid Search Techniques for Context AccuracyЧтениеPractice Knowledge Check: Hybrid and Re-Ranking TechniquesЗадание

Enhancing Context Quality

Hallucinations as a Retrieval ProblemВидеоContext Window ManagementВидеоDemonstration: Reducing Hallucinations via Grounded ContextВидеоDemonstration: Adding Citation References in RAG OutputВидеоImproving Context Relevance and Grounding in RAGЧтениеPractice Knowledge Check: Enhancing Context QualityЗадание

Orchestrating RAG Workflows with LangGraph

Introduction to LangGraphВидеоDemonstration: Building a Stateful RAG Graph with LangGraphВидеоDemonstration: Decision-Driven RAG Orchestration with LangGraph - IВидеоDemonstration: Decision-Driven RAG Orchestration with LangGraph - IIВидеоDesigning Graph-Based LLM Workflows with LangGraphЧтениеPractice Knowledge Check: Orchestrating RAG Workflows with LangGraphЗадание

Module Wrap-Up and Assessment

Module Summary : Building and Optimizing RAG PipelinesЧтениеKnowledge Check: Building and Optimizing RAG PipelinesЗадание
03Deploying and Evaluating RAG Systems28 материалов

RAG Deployment Fundamentals

RAG System Deployment in ProductionВидеоOptimized End-to-End RAG Pipeline and System DesignВидеоDemonstration: Deploying RAG App with Streamlit : RAG Core - Retrieval SetupВидеоDemonstration: Deploying RAG App with Streamlit : RAG Core - Question AnsweringВидеоDemonstration: Deploying RAG App with Streamlit : Data and AuthenticationВидеоDemonstration: Deploying RAG App with Streamlit : Ingestion and Prompt DesignВидеоDemonstration: Deploying RAG App with Streamlit : Context Retrieval and UtilsВидеоDemonstration: Deploying RAG App with Streamlit : Deployment ВидеоDemonstration: Integrating API Endpoints for Retrieval-IВидеоDemonstration: Integrating API Endpoints for Retrieval-IIВидеоDeploying Retrieval-Augmented Generation ApplicationsЧтениеPractice Knowledge Check: RAG Deployment FundamentalsЗадание

Monitoring and Evaluation

Evaluating RAG System PerformanceВидеоBenchmarking RAG PerformanceВидеоDemonstration: Using LangSmith or LlamaIndex for Local EvaluationВидеоDemonstration: Analyzing Cost and Latency MetricsВидеоEvaluating RAG Pipelines: Metrics and Observability ToolsЧтениеPractice Knowledge Check: Monitoring and EvaluationЗадание

Improving Retrieval Accuracy and Scalability

Accuracy vs Scalability in RAGВидеоEnhancing Query Understanding and RankingВидеоDemonstration: Implementing Hybrid Retrieval at ScaleВидеоDemonstration: Optimizing Latency and Throughput for RAG SystemsВидеоScaling RAG Systems for High-Performance ApplicationsЧтениеPractice Knowledge Check: Retrieval Accuracy and ScalabilityЗадание

Module Wrap-Up and Assessment

Production-Ready RAG System ArchitectureВидеоModule Summary : Deploying and Evaluating RAG SystemsЧтениеA Practical Guide to Building Scalable LLM ApplicationsЧтениеKnowledge Check: Deploying and Evaluating RAG SystemsЗадание
04Course Wrap-Up4 материалов

Course Wrap-up and Assessments

Course Summary: RAG Systems in PracticeВидеоPractice Project: Building and Deploying a Scalable RAG SystemЧтениеEnd Course Knowledge Check: RAG Systems in PracticeЗаданиеDescribe your Learning JourneyОбсуждение