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Build RAG Applications
Get Started
Wojciech 'Victor' Fulmyk
and
IBM Skills Network Team
4.5
Based on 84 ratings
Wojciech 'Victor' Fulmyk
and
IBM Skills Network Team
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.
Read more
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.
Read more
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!
Register for this course and see more details by visiting:
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What's inside
Syllabus
Introduction to RAG
This module provides an overview of Retrieval-Augmented Generation (RAG), illustrating how it can enhance information retrieval and summarization for AI applications. The module features a lab designed to introduce the fundamental components of building RAG applications, presented in an easy-to-use Jupyter Notebook format. Through this hands-on project, you’ll learn to split and embed documents and implement retrieval chains using LangChain.
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Syllabus
Introduction to RAG
This module provides an overview of Retrieval-Augmented Generation (RAG), illustrating how it can enhance information retrieval and summarization for AI applications. The module features a lab designed to introduce the fundamental components of building RAG applications, presented in an easy-to-use Jupyter Notebook format. Through this hands-on project, you’ll learn to split and embed documents and implement retrieval chains using LangChain.
Read more
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Build RAG Applications: Get Started.
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Career center
Learners who complete Build RAG Applications: Get Started will develop knowledge and skills
that may be useful to these careers:
Generative AI Developer
Generative AI Developer
A Generative AI Developer specializes in creating applications powered by generative AI models. This course is exceptionally well-suited for aspiring Generative AI Developers, as it directly focuses on building Retrieval-Augmented Generation (RAG) applications—a core technique in this domain. Learners gain hands-on experience moving from project planning to constructing user-friendly Generative AI applications using Python and Gradio. The practical work with LlamaIndex and LangChain to develop a QA bot and a conversation suggestions bot provides the precise skills needed for designing, implementing, and deploying innovative AI-driven solutions that deliver context-aware user experiences.
Conversational Artificial Intelligence Developer
Conversational Artificial Intelligence Developer
A Conversational Artificial Intelligence Developer creates interactive AI agents, chatbots, and virtual assistants. This course offers direct and highly relevant experience for a Conversational Artificial Intelligence Developer, as it guides learners through building a QA bot that can answer questions using information contained in source documents. By exploring RAG fundamentals and developing applications using Python, Gradio, LangChain, and LlamaIndex, you acquire the expertise needed to design and implement AI solutions that provide meaningful, context-aware user interactions, such as those found in sophisticated conversational systems.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
An Artificial Intelligence Engineer is responsible for designing, developing, and deploying intelligent systems and models. The "Build RAG Applications: Get Started" course directly equips you with essential skills for this role by teaching you to build and deploy RAG applications. You will learn to leverage multiple LLMs, utilize frameworks like LangChain and LlamaIndex, and construct end-to-end AI solutions. This deep dive into RAG fundamentals, from enhancing information retrieval to creating context-aware user experiences, is crucial for an Artificial Intelligence Engineer to build robust and effective AI-driven products and services.
AI Applications Developer
AI Applications Developer
An AI Applications Developer focuses on translating artificial intelligence concepts into functional software applications. This course is perfectly aligned with the responsibilities of an AI Applications Developer, providing end-to-end experience in transforming an idea into a fully functional RAG AI solution. You will gain proficiency in using Python, Gradio for user-friendly interfaces, and popular frameworks like LangChain and LlamaIndex. The hands-on labs and project work, including building a QA bot, ensure you acquire the practical skills needed to design, implement, and deploy real-world AI-driven solutions that deliver meaningful and context-aware user experiences.
Natural Language Processing Engineer
Natural Language Processing Engineer
A Natural Language Processing Engineer develops systems that understand, process, and generate human language. RAG applications are fundamentally rooted in natural language processing, making this course an excellent fit for an aspiring Natural Language Processing Engineer. The program covers how RAG enhances information retrieval, involves document chunking, embedding models, and leverages LLMs to generate high-quality, context-aware responses. Engaging with frameworks like LangChain and LlamaIndex to build practical applications directly hones the skills required to innovate in text analysis, comprehension, and generation systems.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer builds, deploys, and maintains machine learning models and systems. The "Build RAG Applications: Get Started" course is highly relevant for a Machine Learning Engineer because Retrieval-Augmented Generation is an advanced machine learning technique. You will gain hands-on experience with RAG pipelines, including splitting and embedding documents, implementing retrieval chains, and leveraging LLMs. This practical training in designing, implementing, and deploying AI-driven solutions that focus on context-aware information retrieval provides a crucial understanding of how to operationalize complex ML models for real-world applications.
Prompt Engineer
Prompt Engineer
A Prompt Engineer specializes in designing, testing, and refining prompts to optimize the performance and output of large language models. While not exclusively a prompting course, "Build RAG Applications: Get Started" provides critical foundational knowledge for a Prompt Engineer. The course delves into how LLMs interact with retrieved information to create context-aware user experiences and covers the implementation of prompt templates. Understanding the mechanics of RAG applications allows you to craft more effective, targeted prompts, enhancing the quality and relevance of LLM-generated responses within retrieval-augmented systems.
AI Solutions Architect
AI Solutions Architect
An AI Solutions Architect designs the overarching structure and components of complex artificial intelligence systems. For an AI Solutions Architect, a deep understanding of RAG fundamentals and frameworks like LangChain and LlamaIndex is paramount for designing robust and scalable AI architectures. This course provides insights into key concepts such as vector databases, embedding models, document chunking, retrievers, and prompt templates. This detailed knowledge of how RAG applications are built and deployed enables you to make informed decisions when structuring and integrating advanced information retrieval capabilities into comprehensive AI solutions.
Applied Scientist Artificial Intelligence
Applied Scientist Artificial Intelligence
An Applied Scientist Artificial Intelligence bridges the gap between fundamental research and the practical application of AI technologies, often requiring an advanced degree. This course provides practical experience in building and deploying RAG applications, demonstrating how theoretical concepts of LLMs and information retrieval are translated into functional AI solutions using industry-standard frameworks like LangChain and LlamaIndex. This hands-on exposure to implementing cutting-edge AI techniques like RAG is essential for an Applied Scientist to validate research findings and productize new AI capabilities effectively, contributing directly to real-world impact.
Research Scientist, Artificial Intelligence
Research Scientist, Artificial Intelligence
A Research Scientist Artificial Intelligence conducts original research to advance the state of the art in artificial intelligence, often requiring an advanced degree. While this course focuses on practical implementation, it provides a crucial foundational understanding of RAG architectures, LLM interactions, and framework comparisons (LangChain versus LlamaIndex). This practical insight into current RAG implementations offers a solid base for a Research Scientist to identify existing challenges, propose novel improvements, and contribute significantly to the evolution of generative AI and intelligent information retrieval techniques, inspiring future research directions.
Machine Learning Operations Engineer
Machine Learning Operations Engineer
A Machine Learning Operations Engineer focuses on the deployment, monitoring, and maintenance of machine learning models in production environments. The "Build RAG Applications: Get Started" course directly addresses the deployment aspect by teaching you to "design, implement, and deploy AI-driven solutions." Learners gain hands-on experience with end-to-end RAG workflows, moving from project planning to constructing functional applications using various frameworks. This practical understanding of how to operationalize RAG applications, ensuring they consistently deliver context-aware user experiences, is invaluable for a Machine Learning Operations Engineer managing AI systems in production.
AI Product Manager
AI Product Manager
An AI Product Manager defines the vision, strategy, and roadmap for artificial intelligence products, bridging technical and business aspects. Understanding the technical capabilities and limitations of RAG applications, the underlying frameworks like LangChain and LlamaIndex, and components such as LLMs and vector databases is vital for an AI Product Manager. This course provides practical knowledge of how RAG applications are built and deployed, enabling you to better define product requirements, assess technical feasibility, anticipate user needs for context-aware experiences, and guide the development of innovative AI-driven products with confidence.
Data Scientist
Data Scientist
A Data Scientist analyzes complex data to extract insights, build predictive models, and develop data-driven solutions. Many Data Scientist roles increasingly involve natural language processing and developing generative AI capabilities. This course provides specific, in-demand skills in RAG, LLM integration, and working with diverse data for intelligent information retrieval. Learning how to build RAG applications, including managing document chunking, embeddings, and retrieval chains, enhances a Data Scientist's ability to tackle advanced text-based analysis, create sophisticated question-answering systems, and leverage generative AI in data-driven projects effectively.
Backend Software Engineer Artificial Intelligence
Backend Software Engineer Artificial Intelligence
A Backend Software Engineer Artificial Intelligence develops the server-side logic, APIs, and data integrations for AI-powered applications. This course provides valuable hands-on experience in constructing RAG pipelines, working with vector databases, and integrating LLMs, which are core components that a Backend Software Engineer would implement and manage within an AI system. Learning about document chunking, retrievers, and prompt templates, and comparing frameworks like LlamaIndex and LangChain, equips you with the practical skills necessary to build robust, scalable, and efficient backend infrastructure for sophisticated AI solutions.
Knowledge Engineer
Knowledge Engineer
A Knowledge Engineer designs, develops, and manages intelligent systems for organizing, representing, and retrieving knowledge. Retrieval-Augmented Generation applications inherently focus on enhancing information retrieval and providing context-aware responses from source documents, directly aligning with the core function of a Knowledge Engineer. This course enables you to learn to implement modern RAG systems by understanding key concepts such as vector databases, embedding models, document chunking, and retrievers, and applying frameworks like LlamaIndex. These skills are crucial for creating dynamic, intuitive, and efficient knowledge management and retrieval solutions.
For more career information including salaries, visit:
OpenCourser.com/course/s8vxyo/build
Reading list
We haven't picked any books for this reading list yet.
Transformers are the fundamental architecture behind most modern Large Language Models used in RAG. provides a comprehensive guide to working with transformers using the Hugging Face ecosystem. It offers essential background knowledge for understanding the generative component of RAG systems.
Provides a comprehensive overview of deep learning for natural language processing, including a chapter on RAG. It valuable resource for both beginners and advanced readers who want to learn more about RAG and its applications.
Provides a comprehensive overview of machine learning for natural language processing, including a chapter on RAG. It valuable resource for both beginners and advanced readers who want to learn more about RAG and its applications.
Provides a comprehensive overview of natural language processing, including a chapter on RAG. It valuable resource for both beginners and advanced readers who want to learn more about RAG and its applications.
Provides a comprehensive overview of text generation, including a chapter on RAG. It valuable resource for both beginners and advanced readers who want to learn more about the use of generation in natural language processing.
Provides a comprehensive overview of neural network methods in natural language processing, including a chapter on RAG. It valuable resource for both beginners and advanced readers who want to learn more about the use of neural networks in natural language processing.
Provides a comprehensive overview of natural language processing, including a chapter on retrieval-augmented generation. It is written by a leading researcher in the field and is suitable for both beginners and experienced practitioners.
Provides a comprehensive overview of deep learning for natural language processing. It covers the latest techniques and best practices, and is written by a leading researcher in the field.
Offers a practical introduction to Natural Language Processing using Python and the NLTK library. It's excellent for beginners to gain hands-on experience with fundamental NLP tasks like text processing and analysis, which are helpful prerequisites for working with RAG systems.
Ce livre fournit un aperçu complet du traitement automatique des langues. Il couvre les dernières techniques et bonnes pratiques, et est écrit par un chercheur de premier plan dans le domaine.
この本は、自然言語処理の理論と実装に関する包括的な概要を提供します。最新の技術とベストプラクティスを網羅しており、この分野の第一人者によって書かれています。
Is specifically designed for beginners interested in Retrieval Augmented Generation (RAG). It aims to introduce the core concepts and guide readers in building basic RAG systems. It serves as a good starting point for those with minimal prior knowledge of RAG.
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Aimed at beginners, this book provides a comprehensive roadmap to understanding Retrieval Augmented Generation (RAG) technology. It covers core principles, architecture, training processes, and real-world applications. This valuable resource for those new to RAG looking to gain foundational knowledge.
Large Language Models are a core component of RAG. provides a practical, hands-on guide to understanding and working with LLMs, covering transformers, tokenizers, and semantic search. It offers essential background knowledge for anyone building RAG systems and valuable reference for practitioners.
For those wanting a deep understanding of the generation component in RAG, this book guides you through building an LLM from scratch. It covers the internal workings, limitations, and customization methods. While challenging, it provides a solid foundation in LLM architecture and training.
This project-based book helps solidify understanding of Large Language Models and their applications, including the use of Vector Databases and LangChain, which are highly relevant to RAG implementation. It provides practical experience through building various LLM projects.
Considered a classic in the field, this book provides a broad and deep understanding of Natural Language Processing. While not specifically about RAG, it lays crucial groundwork in areas like text processing, language models (earlier forms), and information extraction, which are prerequisites for understanding RAG. It is commonly used as a textbook in academic institutions.
This classic textbook provides a comprehensive introduction to the field of Information Retrieval, covering fundamental concepts like indexing, querying, and evaluation. Understanding these principles is essential for grasping the 'Retrieval' aspect of RAG systems. It valuable reference and commonly used in academic settings.
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This foundational textbook offers a comprehensive introduction to deep learning, covering theoretical concepts and practical techniques. Deep learning is integral to both the retrieval (e.g., embeddings) and generation (LLMs) components of RAG. While technically challenging, it must-read for a deep understanding of the underlying models.
Investigates the use of RAG for machine translation. It presents a new approach to neural machine translation that incorporates retrieval, and it shows that this approach can improve the quality of machine translations on a variety of language pairs.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/s8vxyo/build
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Rating
4.5
Effort
3 weeks of study, 2-3 hours/week
Level
Intermediate
Via
Coursera
Institution
IBM
Instructors
Wojciech 'Victor' Fulmyk
IBM Skills Network Team
Session
November 3, 2025
- December 1, 2025
Language
English
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