Expensive Frontier Models produce responses that often look polished, confident, and highly competent. From a distance, the output can appear flawless, well-structured, articulate, and seemingly authoritative. But place it under a magnifying glass and the cracks appear: invented facts, unsupported assumptions, subtle logical gaps, and confident fabrications that only become obvious once you dig deeper. This session explores the “illusion of competence” that emerges when these powerful models receive incomplete prompts or insufficient context. We’ll examine how frontier models fill in the blanks, the kinds of inferences they make, and the common ways they quietly go off the rails, while still sounding completely convincing.
You’ll learn:
- Why surface-level quality can be so deceptive
- How missing context leads models to assume facts and invent coherent-looking but incorrect narratives
- Practical patterns for spotting these hidden imperfections
- Techniques to reduce (though not eliminate) these failure modes through better prompting and richer context

If you’ve ever been impressed by an AI response only to later discover critical flaws, this talk will help you develop a more critical eye - and better practices - when working with the most capable models available today.