Technical Sessions & Highlights :

• Prompt & Context Engineering: Learn to craft expert-level prompts using advanced techniques like Chain-of-Thought, then immediately apply these skills in a hands-on lab to control AI responses for specific tasks
• LLM Finetuning: Understand the fundamentals of the Gemini, Claude, and ChatGPT APIs, then build a functional Python backend server that can receive requests and fetch intelligent responses from these models.
• Building a User Interface: Discover how to use a simple framework like Streamlit to build a web application, and then connect this UI to your backend, allowing users to interact with your AI through a clean interface.
• Beyond Ready-Made Solutions: Conceptually explore advanced customization techniques like Retrieval-Augmented Generation (RAG) and fine-tuning, followed by a lab where you run a pre-built RAG system to see how an AI can answer questions from a custom document.
• End-to-End Product Development: Synthesize all learned skills by designing and building a final, multi-domain AI application (e.g., a text summarizer and code generator) that integrates your custom UI and multi-prompt backend into a single, cohesive product.