Project: Generative AI Applications with RAG and LangChain
This course is part of multiple programs. Learn more.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Generative AI Engineering with LLMs Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
Instructors:
English
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What you'll learn
Create and implement document loaders for various sources
Develop vector databases for efficient embedding storage
Implement RAG techniques for improved information retrieval
Build interactive QA bots using LangChain and LLMs
Create user interfaces with Gradio for model interaction
Optimize text splitting strategies for enhanced performance
Skills you'll gain
This course includes:
0.52 Hours PreRecorded video
7 assignments
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
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There are 3 modules in this course
This project-based course provides hands-on experience in building real-world generative AI applications. Students learn to implement document loading and text splitting using LangChain, create vector databases for storing embeddings, and develop retrievers for efficient document segment fetching. The curriculum includes practical implementation of RAG techniques, setting up Gradio interfaces for model interaction, and constructing QA bots using LLMs. Through guided labs and a final project, learners gain portfolio-ready experience in building complete AI applications.
Document Loader Using LangChain
Module 1 · 2 Hours to complete
RAG Using LangChain
Module 2 · 2 Hours to complete
Create a QA Bot to Read Your Document
Module 3 · 3 Hours to complete
Fee Structure
Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: Generative AI Engineering with LLMs, IBM AI Engineering Professional Certificate
Instructors
Innovator in AI and Data Science Education
Wojciech 'Victor' Fulmyk is an accomplished instructor at IBM, specializing in artificial intelligence and data science education. He teaches several online courses on Coursera, including Generative AI Advance Fine-Tuning for LLMs and Deep Learning with Keras and TensorFlow, where he imparts his extensive knowledge of machine learning and neural networks to a global audience. With a strong academic background as a Ph.D. candidate in Economics at the University of Calgary, Victor combines theoretical insights with practical applications in his teaching. His expertise is further demonstrated through his competitive success on platforms like Kaggle, where he has achieved top rankings in challenging data science competitions. Victor's commitment to advancing the field of AI education is evident in his innovative course offerings that prepare learners for the rapidly evolving tech landscape, making him a valuable asset to both IBM and the broader educational community.
Bridging Theory and Practice in Data Science
Kang Wang is a dedicated Data Scientist Intern at IBM and a PhD candidate at the University of Waterloo, where he focuses on the intersection of theoretical research and practical applications in data science. His work involves extensive data analysis and model development in machine learning and artificial intelligence, aiming to enhance understanding and implementation of these technologies in real-world scenarios. Kang has also contributed to the academic community by teaching courses such as Generative AI Language Modeling with Transformers and Fundamentals of AI Agents Using RAG and LangChain, helping students grasp complex concepts in AI. His commitment to advancing the field is evident through his innovative projects and collaborative efforts, making him a valuable asset in the realm of data science and machine learning.
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