Mastering Generative AI Project: RAG and LangChain App from edX

Ready to put your gen AI engineering skills into action? This hands-on project will challenge you to build your own real-world generative AI application and give you plenty to talk about in interviews!

During this course, you'll deepen your understanding of LangChain document loaders and learn how to upload your own documents from various sources. You'll explore text-splitting strategies to improve model responsiveness and use Watsonx for document embedding. Additionally, you'll leverage a vector database to store document embeddings and LangChain to create a retriever for fetching documents.

As you progress, you'll implement retrieval-augmented generation (RAG), build a QA bot, and set up a Gradio interface to interact with your models. By the end of the course, you'll then have a tangible project that showcases your generative AI engineering expertise to potential employers.

If you’re keen to build some hands-on experience that demonstrates how you’ve mastered key gen AI engineering skills, ENROLL TODAY and get ready to take your AI engineering career to the next level!

What's inside

Learning objectives

Gain valuable practical experience building a real-world gen ai application that you can add to your portfolio and talk about in interviews.
Get hands-on practice loading and processing documents using langchain and applying text-splitting strategies and rag.

Create and manage a vector database for document embeddings and develop a retriever to efficiently fetch documents based on queries.
Develop a gradio interface for model interaction and a qa bot using langchain and llms to answer queries.

Gain valuable practical experience building a real-world gen ai application that you can add to your portfolio and talk about in interviews.
Get hands-on practice loading and processing documents using langchain and applying text-splitting strategies and rag.
Create and manage a vector database for document embeddings and develop a retriever to efficiently fetch documents based on queries.
Develop a gradio interface for model interaction and a qa bot using langchain and llms to answer queries.

Syllabus

Module 1: Document Loader Using LangChain

Learn to load documents using LangChain, apply text-splitting strategies with RAG to improve model responsiveness, and practice these techniques through hands-on labs.

Module 2: RAG Using LangChain

Learn how to store embeddings with vector stores like Chroma DB, use LangChain retrievers, and generate embeddings with watsonx.ai, while practicing document preprocessing and retrieval in hands-on labs.

Module 3: Create a QA Bot to Read Your Document

Learn how to implement RAG for improved retrieval, set up a Gradio interface, and build a QA bot with LangChain and LLMs, while gaining hands-on experience in developing an AI application in the final project.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Provides hands-on experience with LangChain, a valuable tool for building AI applications, which is highly sought after in the field

Develops a Gradio interface, which allows learners to create shareable demos of their machine learning models with customizable inputs and outputs

Teaches retrieval-augmented generation (RAG), which is a technique for improving the accuracy and reliability of generative AI models

Presented by IBM, a company recognized for its contributions to artificial intelligence and its Watson AI platform

Requires learners to use watsonx.ai, which may require learners to create an account and agree to its terms of service

Emphasizes building a portfolio-ready project, which is essential for demonstrating practical skills to potential employers in the AI field

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Mastering Generative AI Project: RAG and LangChain App with these activities:

Review LangChain Fundamentals

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Review the core concepts of LangChain to ensure a solid foundation for building RAG applications.

Browse courses on LangChain

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Read the official LangChain documentation.
Work through introductory LangChain tutorials.
Experiment with basic LangChain components.

Brush Up on Vector Databases

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Revisit the principles of vector databases to better understand how they are used for storing and retrieving document embeddings.

Browse courses on Vector Database

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Study the theory behind vector embeddings.
Explore different vector database options like ChromaDB.
Practice querying vector databases for similar vectors.

Follow a RAG Tutorial

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Work through a complete RAG tutorial to gain hands-on experience with the entire process.

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Find a RAG tutorial that uses LangChain.
Implement the tutorial step-by-step.
Modify the tutorial to use different data sources.

Four other activities

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Read 'Building Applications with LLMs using LangChain'

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Supplement your learning with a dedicated book on LangChain to gain a deeper understanding of the framework.

View Putting Knowledge to Work on Amazon

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Obtain a copy of the book.
Read the book chapter by chapter.
Implement the examples provided in the book.

Read 'Natural Language Processing with Transformers'

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Expand your knowledge of the underlying technology with a book on transformers and NLP.

View Natural Language Processing with Transformers,... on Amazon

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Obtain a copy of the book.
Read the book chapter by chapter.
Focus on the chapters related to transformers and embeddings.

Build a RAG Application for a Specific Domain

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Apply your knowledge to build a RAG application for a domain of your choice, such as legal documents or scientific papers.

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Choose a domain and gather relevant documents.
Implement document loading, embedding, and storage.
Build a QA bot using LangChain and an LLM.
Create a user interface for interacting with the bot.

Contribute to LangChain Documentation

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Contribute to the LangChain open-source project by improving documentation or fixing bugs.

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Identify areas in the LangChain documentation that need improvement.
Submit a pull request with your changes.

Career center

Learners who complete Mastering Generative AI Project: RAG and LangChain App will develop knowledge and skills that may be useful to these careers:

Generative AI Engineer

A Generative AI Engineer designs and implements AI models, often focusing on applications that generate content. This course helps to build a strong foundation for a career as a Generative AI Engineer by providing hands-on experience with building a real-world generative AI application. The course emphasizes document loading with LangChain, text-splitting strategies, and retrieval-augmented generation, all of which are essential skills for a Generative AI Engineer. Through this course, you'll gain practical experience with vector databases, retrievers, and building a QA bot, developing a crucial portfolio project that showcases your abilities.

See salaries and explore the career path for Generative AI Engineer

AI Application Developer

An AI Application Developer builds applications that use artificial intelligence to perform tasks and solve problems. This course directly prepares you for a role as an AI Application Developer by letting you build an entire generative AI application. The course uses LangChain to load and process documents, implement RAG strategies, and create a QA bot with a Gradio interface, all while using real-world documents. By the end of the project, you will have acquired the abilities required for the role of a AI Application Developer and have something to present to demonstrate your expertise.

See salaries and explore the career path for AI Application Developer

Machine Learning Engineer

A Machine Learning Engineer develops machine learning models and systems, including generative models used in artificial intelligence applications. This course will assist you in understanding the requirements of a Machine Learning Engineer by having you build your own generative AI application. Specifically, the course focuses on document processing, retrieval-augmented generation, and vector databases. Machine Learning Engineers need these skills to train and deploy effective models. The hands-on nature of this course and the development of a portfolio project makes it a good choice for those who wish to enter this area.

See salaries and explore the career path for Machine Learning Engineer

AI Specialist

An AI Specialist is a professional with a deep understanding of artificial intelligence and various AI techniques. This course is a good fit for an AI Specialist as it concentrates on generative AI and provides hands-on experience with key AI technologies. The course focuses on document loading, implements RAG systems, and vector databases. As an AI specialist, this particular blend of specific skills can improve your portfolio. An AI Specialist is well-suited to taking this course.

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Computational Linguist

A Computational Linguist develops systems to process and understand human language. This course provides a practical understanding of using LangChain for document loading and text splitting, which are essential aspects of computational linguistics. The course's focus on retrieval-augmented generation also helps in the development of models that can handle large amounts of textual data. As a computational linguist, you would take this course to explore generative AI and understand how it works in practice.

See salaries and explore the career path for Computational Linguist

Natural Language Processing Engineer

A Natural Language Processing Engineer works on models that enable computers to understand and process human language. This course may be useful for a Natural Language Processing Engineer by providing a thorough introduction into document processing, text-splitting strategies, and retrieval-augmented generation. These are all techniques used by NLP Engineers to create applications that can understand and generate useful text. The development of a QA bot using LangChain will enhance your practical skills as a Natural Language Processing Engineer.

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Data Scientist

A Data Scientist extracts insights from data using a variety of data science tools and techniques, also often implementing machine learning models. This course may be useful for a Data Scientist, as it focuses on practical applications of AI, using LangChain to handle document loading and text splitting, which is a skill that is useful for Data Scientists who work with text data. The use of vector databases and the building of a retrieval-augmented generation system using these techniques will assist your projects. This course can help a Data Scientist build a strong portfolio piece.

See salaries and explore the career path for Data Scientist

AI Research Scientist

An AI Research Scientist investigates and develops new AI algorithms and techniques, often working at the cutting edge of AI. While this course provides practical skills, rather than delving into novel research, it may be useful for an AI Research Scientist who also implements these models and techniques. For example, this course's emphasis on building a RAG system and QA bot using LangChain provides valuable hands-on experience with current AI methodologies. This experience allows a AI Research Scientist to better understand the implementation and practical details of current AI methods before exploring new possibilities. An advanced degree is often essential for this role.

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AI Product Manager

An AI Product Manager is responsible for the strategy, roadmap, and execution of AI products. The course may be useful for an AI Product Manager because it provides hands-on experience with real AI applications, particularly RAG systems and QA bots. This includes building interfaces using Gradio and using LangChain. By finishing the course, an AI product manager will have a more complete understanding of the technical aspects of AI products, helping them to make better decisions and improve the product development process.

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Software Engineer

A Software Engineer develops software applications and systems. This course may be useful for a Software Engineer who wants to build AI-powered applications. The course offers important experience in using LangChain, implementing retrieval-augmented generation systems, and developing a user interface with Gradio. These are applicable skills for software engineering projects that involve generative AI. The hands-on project in this course helps build your skills as a Software Engineer, providing relevant experience for real-world applications.

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Solutions Architect

A Solutions Architect designs and implements technology solutions to meet specific business needs. This course may be useful for a Solutions Architect who needs to understand the possibilities of generative AI. The course provides the practical experience in building an end-to-end generative AI application, focusing on loading and processing documents, implementing RAG, and creating a user interface with Gradio. This hands-on experience with these techniques can ensure that a Solutions Architect recommends technology that is grounded in practical realities.

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Data Engineer

A Data Engineer designs, builds, and manages the infrastructure that supports data storage and processing. While this course focuses primarily on the application level, it may be useful for a Data Engineer who wishes to understand how data is used in generative AI systems. The course’s focus on LangChain document loaders, vector databases, and document processing provides a closer look at how data is managed in AI applications, thus giving more familiarity to the Data Engineer. The practical RAG system building experience is also relevant for those who may be managing these kinds of systems.

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Technical Project Manager

A Technical Project Manager manages projects, ensuring that they are completed in a timely manner and within budget. This course may be useful for a Technical Project Manager who works on AI projects. The course provides a practical example of an entire AI project, from loading data to creating a user interface. This hands-on view of managing data, choosing technologies such as LangChain, creating a QA bot, and using Gradio helps inform a Technical Project Manager about the technology and implementation realities of AI projects. The final deliverable also serves as an example when planning new projects.

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Technical Consultant

A Technical Consultant advises organizations on technology strategies and solutions. This course may be useful for a Technical Consultant who works with AI systems. The course provides practical insights into the development and implementation of generative AI applications, which can directly inform recommendations from the Technical Consultant. Through learning about document processing, RAG, and building a QA bot, the Technical Consultant gains insight into the feasibility of various implementation strategies. This course also results in a practical project, which can serve as an example.

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AI Trainer

An AI Trainer creates training data and procedures for artificial intelligence models. While this course does not directly train AI models, it may be useful for someone who needs to train AI models that leverage document processing and RAG systems. The course provides a practical view of how data is handled and processed using LangChain, and demonstrates important concepts such as text-splitting strategies. The building of a QA bot will enhance your understanding of how these systems are implemented, which can help an AI Trainer to better understand the data and system requirements for good performance.

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Mastering Generative AI Project

RAG and LangChain App

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