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

本课程是 LLM Engineering: Prompting, Fine-Tuning, Optimization & RAG 专项课程的一部分

位教师：Edureka

包含在中

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4个模块

深入了解一个主题并学习基础知识。

初级等级

推荐体验

1 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

4个模块

深入了解一个主题并学习基础知识。

初级等级

推荐体验

1 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

您将学到什么

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.

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本课程是 LLM Engineering: Prompting, Fine-Tuning, Optimization & RAG 专项课程专项课程的一部分

在注册此课程时，您还会同时注册此专项课程。

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该课程共有4个模块

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.

涵盖的内容

13个视频5篇阅读材料4个作业1个讨论话题

13个视频总计72分钟

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

5篇阅读材料总计85分钟

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

4个作业总计48分钟

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

1个讨论话题总计10分钟

Introduce Yourself 10分钟

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.

涵盖的内容

16个视频5篇阅读材料5个作业

16个视频总计96分钟

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

5篇阅读材料总计120分钟

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

5个作业总计54分钟

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

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.

涵盖的内容

19个视频5篇阅读材料4个作业

19个视频总计100分钟

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

5篇阅读材料总计105分钟

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

4个作业总计48分钟

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

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.

涵盖的内容

1个视频1篇阅读材料1个作业1个讨论话题

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常见问题

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.