AI Application Project with RAG and LangChain
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Course
AI Application Project with RAG and LangChain
Build a real-world generative AI app using LangChain, RAG, and vector databases. Gain hands-on skills to boost your AI career and impress in interviews.
Self-Paced
Mentored
Intermediate
Duration
Generative AI is reshaping how organizations create intelligent applications, from automating customer support to enhancing knowledge management. With the rising demand for AI-driven solutions, professionals who can build real-world applications using frameworks like LangChain and methods such as retrieval-augmented generation (RAG) are in high demand.
In this intermediate-level course, you will develop practical skills for building AI applications with LangChain and IBM watsonx. You will learn strategies for document loading and text splitting, explore embedding documents with watsonx, and use LangChain to fetch and manage documents from multiple sources.
As you progress through the course, you will implement RAG to improve model responsiveness, set up vector databases for storing embeddings, and build a question-answering bot that retrieves information directly from loaded documents. Plus, you will work on practical labs and projects that will give you the opportunity to apply these skills in practice.
For individuals looking to advance their careers with practical experience in building AI applications, this course provides a strong foundation in working with RAG and LangChain.
This course comprises three purposely designed modules that take you on a carefully defined learning journey.
It is a self-paced course, which means it is not run to a fixed schedule with regard to completing modules. It is anticipated that you will complete the course in 9 hours. However, as long as the course is completed by the end of your enrollment, you can work at your own pace. And dont worry, youre not alone! You will be encouraged to stay connected with your learning community through the course discussion space.
The materials for each module are accessible from the start of the course and will remain available for the duration of your enrollment. Methods of learning and assessment will include discussion space, videos, reading material, quizzes, hands-on labs, quizzes and final assignment.
Once you have successfully completed the course, you will earn your IBM Certificate.
You will be able to:
- Learn to build real-world generative AI applications
- Master LangChain for document processing and improve model performance through text splitting techniques.
- Configure vector databases to efficiently manage and retrieve document embeddings.
- Build an interactive Gradio interface and a QA bot, equipping you with practical skills in AI-driven customer support.
- Developers or AI practitioners with basic Python skills.
- Those aiming to build real-world Generative AI applications.
- Professionals interested in RAG (Retrieval-Augmented Generation), LangChain, vector databases, and Gradio interfaces.
This course requires a basic knowledge of Python.
Course Outline
Why Learn with SkillUp Online?
We believe every learner is an individual and every course is an opportunity to build job-ready skills. Through our human-centered approach to learning, we will empower you to fulfil your professional and personal goals and enjoy career success.
Reskilling into tech? We’ll support you.
Upskilling for promotion? We’ll help you.
Cross-skilling for your career? We’ll guide you.
Personalized Mentoring & Support
1-on-1 mentoring, live classes, webinars, weekly feedback, peer discussion, and much more.
Practical Experience
Hands-on labs and projects tackling real-world challenges. Great for your resumé and LinkedIn profile.
Best-in-Class Course Content
Designed by the industry for the industry so you can build job-ready skills.
Job-Ready Skills Focus
Competency building and global certifications employers are actively looking for.
FAQs
You will learn step by step how to implement a Retrieval-Augmented Generation pipeline with LangChain, embeddings, and vector databases. This will guide you in creating AI Application Projects that use real-world workflows.
The course demonstrates how to embed documents, store them in a vector database for AI, and retrieve relevant segments to improve model responses. This hands-on approach helps you understand the foundation of RAG AI Projects.
You will gain skills in building Generative AI RAG solutions, applying text splitting techniques in LangChain, configuring vector databases, embedding documents with Watsonx, and creating AI-powered QA bots with Gradio interfaces.
Yes, you should have a basic knowledge of Python before starting. The course focuses on applying Python within LangChain workflows rather than teaching Python fundamentals.
You will work with LangChain, Watsonx embeddings, vector databases, advanced retrievers, and Gradio for interface building. These tools help you create AI-powered document processing applications and QA bots.
Yes, you will complete multiple RAG AI Projects, including building a QA bot that leverages LangChain and large language models to answer document-based questions.
LangChain integrates with vector databases for AI to store embeddings and retrieve the most relevant document segments. This enables accurate and responsive RAG projects.
Text splitting breaks documents into smaller, manageable chunks for better embedding and retrieval. It enhances the effectiveness of Generative AI RAG pipelines.
Yes. You will embed documents using Watsonx models and integrate them into LangChain workflows for improved retrieval and responsiveness.
Absolutely. The course covers advanced retriever techniques in LangChain, allowing you to optimize your RAG with LangChain applications.
Gradio is used to create simple yet interactive web interfaces. You will build a Gradio app to test your QA bot, making your AI application projects user-friendly.
Yes. You will construct a fully functional QA bot using LangChain and LLMs to answer questions based on your uploaded documents.
Yes. You will explore the differences between fine-tuning and RAG implementation in AI, understanding when each approach is best suited for real-world Generative AI projects.
The skills you gain in this course allow you to build QA bots and customer support solutions using LangChain, embeddings, and retrieval pipelines.
Yes. The course includes multiple labs and a final project, giving you practical experience in AI Application Projects.
Course Offering
Type of certificate
IBM Certificate
About this course
03 Modules
04 Skills
Includes
Discussion space
08 Hands-on labs
03 Practice quizzes
02 Graded quizzes
01 Final project
Create
Vector Database to Store Document Embeddings
Retriever to Fetch Document Segments Based on Queries
QA Bot to Read Your Document
Exercises to explore
Document Loader Using LangChain
Embed Documents Using Watsonx’s Embedding Model
Simple Gradio Interface to Interact with Your Models
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