An AI system can usually explain what it produced. That does not mean it will reveal everything it decided along the way.

Most meaningful tasks contain trade-offs. The model may choose simplicity over extensibility, speed over precision, consistency over local optimization, or one implementation path over several reasonable alternatives.

The final result may look good while hiding the fact that a consequential decision was made silently.

As AI-generated work becomes a larger part of software and product development, reviewing only the final output is no longer enough. Teams need a way to uncover the assumptions, alternatives, and trade-offs embedded inside it.

In this session, I’ll share the framework I use to expose those hidden decisions. We’ll look at how to make an LLM generate alternative paths, identify the dimensions it balanced, surface assumptions it treated as facts, and reveal relevant considerations it may have omitted.

The goal is not to eliminate trade-offs. It is to ensure that humans know which trade-offs they are accepting.

Target audience: Software engineers, product engineers, architects, tech leads, and teams reviewing AI-generated work.
Level: Intermediate.
Preferred duration: 30–45 minutes.
Format: Practical framework with examples from software and product-development decisions.
Prerequisites: Basic experience using LLMs for complex or open-ended work.
Source: Lessons from reviewing AI-generated outputs and exposing silent decisions, assumptions, and alternatives.