Why Your AI Solves the Wrong Problem (And How Intent Engineering Fixes It)

TL;DR AI systems don't usually fail because the model is wrong. They fail because the system solved the wrong problem correctly. Intent engineering is the layer that closes the gap between what you say and what you actually mean. It ensures the system is solving the right problem before execution begins. Most failures come from misalignment, not capability: The model follows instructions literally, even when the intent is different Missing constraints lead to wrong assumptions Systems optimize for the wrong definition of success The solution is to treat intent as a contract: Define the goal (what outcome you actually want) Specify constraints (what must not change) Set success criteria (how you verify correctness) Define failure boundaries (what should never happen) In practice, intent engineering follows a simple workflow: Raw Intent → Expand → Contract → Execute → Verify When intent is clear, systems become predictable and reliable. When it is not, even powerful models will confident