Ementorhub

What you'll learn

Gain practical experience building your own real-world generative AI application to showcase in interviews

Create and configure a vector database to store document embeddings and develop a retriever to fetch relevant segments based on user queries

Set up a simple Gradio interface for user interaction and build a question-answering bot using LangChain and a large language model (LLM)

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