Applied AI Systems
Decide how the system should think before deciding what to ask it.
Design context assembly, tool contracts, and behavioral bounds so output is dependable enough to act on.
A frontier model will produce something for almost any input. Whether that output can be acted on depends on decisions made around the model: what context it receives, which tools it can call, what it is permitted to assert, and what it should do when the evidence is thin.
This is where prompt work gets mistaken for the whole job. A prompt is edited until the demo passes, then behavior shifts under real inputs. Nobody can say why the system answered differently this time, because the behavior was never specified, only discovered.
Model Behavior Design treats those decisions as architecture. It gives the team a clearer way to assemble context, define tool contracts, set the limits of what the system may claim, and specify how it degrades under uncertainty. Prompts are one mechanism here, not the headline. What matters next is behavior that holds across real inputs and can be explained when it changes.
Let’s get going
- Start where the output cannot be trusted — Pick one task where the system is fluent but unreliable, and specify what a correct response is allowed to contain.
- Design the context, not just the instruction — Decide what the model sees, in what order, and from where, before tuning the wording of the request.
- Bound what the system may assert — Define how it signals uncertainty, when it declines, and what it must cite, so confident wrong answers become visible rather than persuasive.
Outcomes
- Dependable output — Behavior holds across real inputs rather than passing a demo and drifting in production.
- Explainable changes — When behavior shifts, the team can point to the context, tool, or constraint that moved rather than guessing at the prompt.
- Honest uncertainty — The system reports what it does not know instead of asserting past the evidence.