About this Course
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! 3b:T
What You'll Learn
- 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.
Prerequisites
- Prerequisite: To enroll for this course, we recommend that you have completed all the courses in the IBM Generative AI Engineering Professional Certificate