Problem Statement: Build an AI-powered assistant that takes learners from the fundamentals of Large Language Models to advanced, real-world applications. The project covers core concepts such as tokenization, embeddings, and model parameters, followed by hands-on implementation of LLM APIs, conversational interfaces, and multi-step prompt workflows.

Learners will evaluate and fine-tune models, apply Retrieval-Augmented Generation (RAG) for knowledge grounding, and ultimately design intelligent, agent-based systems capable of reasoning, tool selection, and accurate responses across both unstructured documents and structured data sources.