Project: Generative AI Applications with RAG and LangChain
This course is part of multiple programs. Learn more
Instructors: Kang Wang +1 more
Instructor ratings
We asked all learners to give feedback on our instructors based on the quality of their teaching style.
What you'll learn
Skills you'll gain
There are 3 modules in this course
You’ll begin by filling in key knowledge gaps, such as using LangChain’s document loaders to ingest documents from various sources. You’ll then explore and apply text-splitting strategies to improve model responsiveness and use IBM watsonx to embed documents. These embeddings will be stored in a vector database, which you’ll connect to LangChain to develop an effective document retriever. As your project progresses, you’ll implement retrieval-augmented generation (RAG) to enhance retrieval accuracy, construct a question-answering bot, and build a simple Gradio interface for interactive model responses. By the end of the course, you’ll have a complete, portfolio-ready AI application that showcases your skills and serves as compelling evidence of your ability to engineer real-world generative AI solutions. If you're ready to elevate your career with hands-on experience, enroll today and take the next step toward becoming a confident AI engineer.
RAG Using LangChain
Create a QA Bot to Read Your Document
Explore more from Machine Learning
©2025 ementorhub.com. All rights reserved