▪ Understanding Prompt Engineering techniques to optimize interactions and outputs from Generative AI models.

▪ Learning Deep Learning fundamentals with hands-on implementation of Neural Networks, RNNs, and LSTM models using Python.

▪ Exploring Retrieval-Augmented Generation (RAG), its architecture, components, workflow, and accuracy measurement techniques.

▪ Building, testing, and validating a real-time RAG application using Python and real-world datasets.