Practical, Hands-on Training Delivered Live
Building on more than 20 years of top-notch instruction from Statistical Horizons, AI Horizons offers training in AI, large language models, and agentic workflows for researchers, equipping them with the practical skills needed to use these tools effectively in real-world projects and work.
Cutting-edge. Actionable. Accessible.
Take Your AI Skills to the Next Level
AI Horizons provides hands-on training in applied and agentic AI methods that are rapidly becoming essential for research, analytics, and data-intensive work. Our seminars focus on how to use large language models (LLMs), like ChatGPT, Claude, and Gemini, as well as AI agents and related tools, effectively in real workflows—while maintaining rigor, transparency, appropriate human oversight, and sound analytic practice.
AI Horizons is part of the Statistical Horizons family, founded by Paul Allison in 2005. Since then, we’ve helped thousands of researchers learn the latest statistical methods from the top instructors in their fields. AI Horizons brings that same standard of excellence to applied AI, helping researchers across disciplines build a relevant, well-rounded toolkit.
Knowledge. Preparation. Efficiency.
AI Seminars and Trainings
AI Horizons offers short seminars on timely large language model and applied AI topics for research and analytics, including prompt engineering, context management, agent harness design, agentic and multistep workflows, text-focused methods, and AI-assisted workflows in tools such as R, Stata, and Python. Our courses provide clear, thorough instruction in an efficient format, so you can build new skills without the time commitment of a semester-long course. You’ll leave prepared to apply these methods confidently in research projects.
Relevance. Innovation. Excellence.
What You’ll Learn
AI Horizons seminars help researchers, analysts, and practitioners move from trying AI tools to using them systematically in real work. You’ll learn applied techniques for LLM-supported and agentic workflows, including how to design multistep tasks, connect specialized tools or agents, and determine where human review is needed. Topics also include prompting and working with text at scale—including classification, extraction, summarization, coding, and labeling—along with human-in-the-loop methods, reliability checks, and responsible use practices.
Our courses are designed to be accessible but rigorous, with a focus on choosing the right AI approach, managing context effectively, building and maintaining reliable agent harnesses and repeatable workflows, evaluating agent outputs and intermediate steps, validating results carefully, and communicating AI-assisted work clearly.