AI Con USA 2026 - Prompt Engineering

Although there are several controversies and misunderstandings surrounding AI and machine learning, one thing is apparent — people have quality concerns about the safety, reliability, and trustworthiness of these types of systems. Not only are ML-based systems shrouded in mystery due to their largely black-box nature, they also tend to be unpredictable since they can adapt and learn new things at runtime. Validating ML systems is challenging and requires a cross-section of knowledge, skills, and experience from areas such as mathematics, data science, software engineering, cyber-security,...

AI agents have existed for decades, but generative AI has fundamentally changed what agents can do and how they are designed and built. Come and explore the evolution of AI agents across two major waves. First, learn the foundations of Agentic AI through agents built using rules, heuristics, and traditional machine learning, examining where these approaches excel and why they struggle with complexity, ambiguity, and scale. Then dive into the second wave of agents powered by generative AI and multimodal large language models. These modern agents can reason, plan, use tools, and interact...

The success of agentic applications using LLMs depends largely on the ability to properly manage the context - the collection of prompts, tools, history, memory, and RAG-indexed content. When you take all of these elements into account, you are able to get the most out of the LLM while avoiding hallucination, conversational drift, and relevance issues. Topics to be covered include: dynamic LLM selection, automated prompt development, context compression, tool decoration, RAG optimization, and LLM-as-a-judge quality assessment of LLM responses. Each of these smaller parts together build...