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 and Agentic AI Capstone Project · LearnSpace
Назад в каталог
courseraПрограммирование

RAG and Agentic AI Capstone Project

Курс от IBM
Продвинутый≈ 14.6 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

Demonstrate you have the job-ready skills to design and implement a complete AI system from data to deployment, with this portfolio-worthy RAG and Agentic AI Capstone Project from IBM. You’ll design and build a production-style multimodal RAG system that combines structured data, embeddings, retrieval logic, evaluation strategies, and intelligent workflows into one cohesive, scalable solution. You’ll create and manage structured JSON datasets, generate text and image embeddings, and construct a vector database to power accurate similarity search and metadata-filtered retrieval. As you progress, you’ll implement robust RAG pipelines, apply re-ranking and evaluation techniques, and strengthen response quality using multimodal inputs and systematic validation approaches. You’ll also design a multi-agent recommendation system, integrate tools using the Model Context Protocol (MCP), orchestrate workflow testing, and launch an interactive Gradio chatbot interface. By the end, you’ll have developed an end-to-end generative AI application that demonstrates practical AI engineering expertise, architectural thinking, and production-ready implementation skills.

Навыки, которые вы освоите

Multimodal PromptsRetrieval-Augmented GenerationAgentic systemsLLM ApplicationEmbeddingsVector DatabasesAgentic WorkflowsTool CallingJSONAI WorkflowsGenerative AISystem TestingModel Context ProtocolLarge Language ModelingGenerative AI AgentsUnstructured DataAI Orchestration

Программа курса

5 модулей · 54 учебных материалов

01Module 1: Build a Structured Generative AI Application15 материалов

Welcome to the Course

Course IntroductionВидеоCourse OverviewЧтениеProject OverviewВидеоReading: Helpful Tips for Course CompletionPLUGIN

Lesson 1: Structure Text Data with LLMs

Учитесь у экспертов

Abdul Fatir

Преподаватель курса

Tenzin Migmar

Преподаватель курса

Jianping Ye

Преподаватель курса

Zikai Dou

Преподаватель курса

RAG and Agentic AI Capstone Project
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

≈ 14.6 ч

5 модулей

Язык: Английский

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

Часть программы вашего университета
Reading: Assignment Overview: Structure Unstructured Restaurant Data with an LLMPLUGIN
Lab: Structure Unstructured Restaurant Data with an LLMВнешний инструмент
Checklist: Structure Text Data with LLMsЗадание

Lesson 2: Process Multimodal Customer Data with LLMs

Reading: Assignment Overview: Process Multimodal Data with LLMsPLUGINLab: Process Multimodal Data with LLMsВнешний инструментChecklist: Process Multimodal Customer Data with LLMsЗадание

Lesson 3: Build a Simple Interactive User Interface

Reading: Assignment Overview: Build a Command-Line Data Management UI for Restaurant DataPLUGINLab: Build a Command-Line Data Management UI for Restaurant DataВнешний инструментChecklist: Build a Simple Interactive User InterfaceЗадание

Lesson 4: Module Summary and Assessment

Podcast: Recap: Build a Structured Generative AI ApplicationPLUGINGraded Quiz: Build a Structured Generative AI ApplicationЗадание
02Module 2: Design a Multimodal RAG System 11 материалов

Lesson 1: Multimodal Vector Index Construction

Reading: Assignment Overview: Construct a Multimodal Vector IndexPLUGINLab: Construct a Multimodal Vector IndexВнешний инструментChecklist: Multimodal Vector Index ConstructionЗадание

Lesson 2: Similarity Retrieval with Metadata Filtering

Reading: Assignment Overview: Similarity Retrieval with Metadata FilteringPLUGINLab: Similarity Retrieval with Metadata FilteringВнешний инструментChecklist: Similarity Retrieval with Metadata Filtering Задание

Lesson 3: Multimodal Similarity Fusion and Ranking

Reading: Assignment Overview: Multimodal Similarity Fusion and Retrieval RankingPLUGINLab: Multimodal Similarity Fusion and Retrieval RankingВнешний инструментChecklist: Multimodal Similarity Fusion and Ranking Задание

Lesson 4: Module Summary and Assessment

Podcast: Recap: Design a Multimodal RAG System PLUGINGraded Quiz: Design a Multimodal RAG SystemЗадание
03Module 3: Combine Agents into a Multi-Agent System 12 материалов

Lesson 1: Define Agents and Their Roles

Reading: Assignment Overview: Design Specialized Agents for a Recommendation SystemPLUGINLab: Design Specialized Agents for a Recommendation SystemВнешний инструментChecklist: Define Agents and Their RolesЗадание

Lesson 2: Integrate Agents into a Multi-Agent System

Reading: Assignment Overview: Implement and Test a Multi-Agent Recommendation SystemPLUGINLab: Implement and Test a Multi-Agent Recommendation SystemВнешний инструментPitching a Multimodal Travel Recommendation SystemDIALOGUEChecklist: Integrate Agents into a Multi-Agent System Задание

Lesson 3: Build a Chatbot Interface for the Recommendation System

Reading: Assignment Overview: Build a Chatbot Interface for the Recommendation System PLUGINLab: Build a Chatbot Interface for the Recommendation SystemВнешний инструментChecklist: Build a Chatbot Interface for the Recommendation SystemЗадание

Lesson 4: Module Summary and Assessment

Podcast: Recap: Combine Agents into a Multi-Agent SystemPLUGINGraded Quiz: Combine Agents into a Multi-Agent SystemЗадание
04Module 4: Integrate Agents, RAG, and Tools with MCP11 материалов

Lesson 1: Organize Tools and Data in an MCP Server

Reading: Assignment Overview: Build an MCP ServerPLUGINLab: Build an MCP ServerВнешний инструментChecklist: Organize Tools and Data in an MCP ServerЗадание

Lesson 2: Implement an MCP Client for Server Communication

Reading: Assignment Overview: Build an MCP Client PLUGINLab: Build an MCP ClientВнешний инструментChecklist: Implement an MCP Client for Server CommunicationЗадание

Lesson 3: Design an LLM-based MCP Host

Reading: Assignment Overview: Build a Full MCP ApplicationPLUGINLab: Build a Full MCP ApplicationВнешний инструментChecklist: Design an LLM-based MCP HostЗадание

Lesson 4: Module Summary and Assessment

Podcast: Summary: Integrate Agents, RAG, and Tools with MCPPLUGINGraded Quiz: Integrate Agents, RAG, and Tools with MCPЗадание
05Module 5: Final Project and Course Wrap-Up5 материалов

Lesson 1: Final Project

Reading: Prepare to Submit Your ProjectPLUGINProject: Final Project Submission and EvaluationВнешний инструмент

Lesson 2: Course Wrap-Up

Course Wrap-UpВидеоCongratulations and Next Steps ЧтениеThanks from the Course TeamЧтение