Getting an AI-generated image to capture nuanced, context-specific details—such as authentic cultural references—can be surprisingly difficult. In this session, we’ll explore a practical, reasoning-driven workflow for bridging that gap. The process begins by constructing a detailed persona for a subject-matter expert (for example, a historian of Japanese culture). Using a reasoning engine, we then generate prompts from the expert’s perspective, feed them into an image generation model, and finally, apply AI-based analysis to evaluate the result—closing the loop with metacognitive feedback. I’ll share real-world examples where this approach produced strikingly authentic and context-aware images, along with tips for adapting it to your own creative and professional use cases.

Ajit Jaokar is a Lectu Engineering Sciences at the University of Oxford, where he also serves as Course Director for the "Artificial Intelligence: Cloud and Edge Implementations" program. As an AI Ambassador at Oxford, he leads interdisciplinary AI reskilling initiatives and is actively shaping the future of AI education. Ajit is a Senior AI Fellow at the UK Ministry of Justice’s Justice AI Unit, advising on the national rollout of large-scale AI systems. He is the founder of Erdos Research and feynlabs, and his work focuses on early-stage AI prototypes and democratizing AI through accessible education. Ajit is a frequent speaker at global forums including the World Economic Forum, the European Parliament, and the G7. A passionate advocate for neurodiversity, he is currently writing a book on teaching AI through mathematical foundations to high school students.