Shubhangi Goyal

In this session, I will explore how agents are built and use dynamic context to execute complex tasks and deliver real value. This also includes using context engineering to move beyond single prompt based interactions. The key takeaways would be : - Context engineering, manage memory tools, and tasks for adaptive behaviour - Agent architecture, LLMs and feedback loops - Prompt engineering into the larger system - Real world use case and common pitfalls when deploying AI agents

Learn how to design ML-ready data models inside BI environments. This session covers feature engineering, temporal modelling, unstructured data integration, and semantic layers to enable scalable, explainable AI without common modelling pitfalls.

This session explores how to design data models that support machine learning within BI environments. It covers techniques for feature engineering, temporal modelling, and integrating unstructured data.

In today’s data-driven world, storytelling with Business Intelligence (BI) is the key to turning complex data into impactful narratives. This session explores how to craft compelling stories using data visualizations that drive understanding and action.

Large Language Models (LLMs) have revolutionised how applications are designed, offering capabilities such as natural language understanding, content generation, and conversational engagement. Building LLM-powered applications involves strategic design to leverage these capabilities effectively, focusing on context-awareness, domain-specific fine-tuning, and user-centred workflows. Integrating APIs, data pipelines, and feedback loops during development ensures that the application evolves and adapts to user needs, enhancing performance over time. A critical aspect of LLM application development is response evaluation, which ensures outputs are accurate, coherent, and ethically aligned. Employing techniques like automated scoring, human-in-the-loop feedback, and real-world testing helps refine responses and maintain quality. Addressing challenges such as bias, and alignment with organizational goals ensures that LLM applications are not only functional but also trustworthy. This comprehensive approach to design, development, and evaluation allows organizations to build impactful LLM applications that deliver meaningful interactions across various use cases.