Note: This module was specifically tailored for the live cohort during the bootcamp. If you're accessing this coursework after the bootcamp has concluded, it is not applicable for you.
Last updated
As we approach the conclusion of this mini bootcamp, it's time to transform your acquired knowledge into practical applications.
To guide you, we've carefully selected a range of exciting project tracks. These projects are your platform for innovation and making a tangible impact! 🌟
To qualify for your bootcamp certificate, complete the required MCQs—one in the Vector Embeddings module and another in the RAG module.
Concurrently, you're expected to build a real-time, RAG-based LLM application.
Suppose the idea of creating an LLM application from the ground up (like the one we saw in the Amazon Discounts case) feels overwhelming. In that case, you can build upon the "Dropbox Retrieval App" example discussed earlier by tailoring it to meet specific needs. For example, you can construct an application with substantial business or social value, like the EU AI Act showcase, which successfully repurposed the Dropbox Retrieval App example to simplify understanding complex legal documents in the AI domain.
That said, there are added incentives (beyond learning) to make a novel application using the LLM App.
What are those incentives? Let's read.