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Build RAG Applications: Get Started

Курс от IBM
Средний≈ 7 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

Data Scientists, AI Researchers, Robotics Engineers, and others who can use Retrieval-Augmented Generation (RAG) can expect to earn entry-level salaries ranging from USD 93,386 to USD 110,720 annually, with highly experienced AI engineers earning as much as USD 172,468 annually (Source: ZipRecruiter). In this beginner-friendly short course, you’ll begin by exploring RAG fundamentals—learning how RAG enhances information retrieval and user interactions—before building your first RAG pipeline. Next, you’ll discover how to create user-friendly Generative AI applications using Python and Gradio, gaining experience with moving from project planning to constructing a QA bot that can answer questions using information contained in source documents. Finally, you’ll learn about LlamaIndex, a popular framework for building RAG applications. Moreover, you’ll compare LlamaIndex with LangChain and develop a RAG application using LlamaIndex. Throughout this course, you’ll engage in interactive hands-on labs and leverage multiple LLMs, gaining the skills needed to design, implement, and deploy AI-driven solutions that deliver meaningful, context-aware user experiences. Enroll now to gain valuable RAG skills!

Навыки, которые вы освоите

Retrieval-Augmented GenerationEmbeddingsPrompt EngineeringVector DatabasesPrompt PatternsData ProcessingLLM ApplicationAI WorkflowsLarge Language ModelingModel DeploymentDocument Management

Программа курса

3 модулей · 33 учебных материалов

01Introduction to RAG14 материалов

Welcome to the Course

Course IntroductionВидеоCourse OverviewЧтениеRAG and Agentic AI Professional Certificate OverviewВидеоReading: Helpful Tips for Course CompletionPLUGIN

What is RAG?

Учитесь у экспертов

Wojciech 'Victor' Fulmyk

Преподаватель курса

IBM Skills Network Team

Преподаватель курса

Build RAG Applications: Get Started
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Обучение на Coursera

≈ 7 ч

3 модулей

Язык: Английский

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

Часть программы вашего университета
Why RAG?Видео
More RAG DetailsВидео
What is RAG?Чтение
Understanding the Why and How of RAGDIALOGUE
Summarize Private Documents Using RAG, LangChain, and LLMsВнешний инструмент
Building the Case: Explaining RAG Architecture to Your Product StakeholderDIALOGUE
Practice Quiz: What is RAG? Задание

Summary and Evaluation

Summary and Highlights: Introduction to RAGЧтениеCheat Sheet: Introduction to RAG ЧтениеGraded Quiz: Introduction to RAGЗадание
02Build Apps with RAG 8 материалов

Create an Interactive RAG Application with a User-Friendly Gradio Interface

Getting Started with Gradio ВидеоReading: Introduction to Gradio PLUGINLab: Set Up a Simple Gradio Interface to Interact with Your ModelsВнешний инструментLab: Construct a QA Bot that Leverages the LangChain and LLM to Answer Questions from Loaded DocumentВнешний инструментPractice Quiz: Building Apps with RAG Задание

Summary and Evaluation

Summary and Highlights: Building Apps with RAG ЧтениеCheat Sheet: Building Apps with RAGPLUGINGraded Quiz: Building Apps with RAG Задание
03Build RAG Apps with LlamaIndex11 материалов

Application Development with LlamaIndex

Intro to LlamaIndex: Document Ingestion and ChunkingВидеоIntro to LlamaIndex: From Vector Stores to Query EnginesВидеоReading: LangChain and LlamaIndex ComparedPLUGINLab: Build an AI Icebreaker Bot with IBM Granite 3.0 & LlamaIndexВнешний инструментPractice Quiz: Application Development with LlamaIndex Задание

Summary and Evaluation

Summary and Highlights: Build RAG Apps with LlamaIndexЧтениеCheat Sheet: Build RAG Apps with LlamaIndexPLUGINGraded Quiz: Build RAG Apps with LlamaIndex Задание

Course Wrap Up

Course Wrap-Up ВидеоCongratulations and Next StepsЧтениеTeam and AcknowledgmentsЧтение