Project: Generative AI Applications with RAG and LangChain

Overzicht

Get ready to put all your generative AI engineering skills into practice! This guided project will test and apply the knowledge and understanding you’ve gained throughout the previous courses in the program.

You will build your own real-world generative AI application.

During this course, you will fill the final gaps in your knowledge to extend your understanding of document loaders from LangChain. You will then apply your new skills to uploading your own documents from various sources.

Next, you will look at text-splitting strategies and use them to enhance model responsiveness.

You will use watsonx to embed documents, a vector database to store document embeddings, and LangChain to develop a retriever to fetch documents. As you work through your project, you will also implement Retrieval Augmented Generation (RAG) to improve retrieval, create a QA bot, and set up a simple Gradio interface to interact with your models.

By the end of the course, you will have a hands-on project that provides engaging evidence of your generative AI engineering skills that you can discuss in interviews.

If you’re ready to add some real-world experience to your portfolio, enroll today and fuel your AI engineering career.

University:

Coursera

Provider:

Coursera

Categories:

Generative AI Courses, LangChain Courses, Vector Databases Courses, Gradio Courses, Retrieval Augmented Generation (RAG) Courses, Embeddings Courses

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