Directional Prompting

Two layers, both required.

Layer 1 — Outcome. Open with a block that names the destination. The goal, what "done" looks like, when to stop, the true invariants. This is the frame.

Layer 2 — Direction. Inside that frame, every sentence names the path forward with positive verbs. "Trace", "build", "use", "read", "return", "ask", "check". The correct behavior is described so clearly and completely that the wrong behavior has no room to exist.

Outcome without direction reads as wishful — the model knows where to go but not how to step. Direction without outcome wanders — the model walks crisp paths to nowhere. Both layers together: a model that knows the destination and walks toward it on every token.

Why both

Modern frontier models (Claude Opus 4.7, GPT-5.5) follow instructions literally. The Claude 4.7 guide: "Positive examples showing how Claude can communicate with the appropriate level of concision tend to be more effective than negative examples or instructions that tell the model what not to do." The GPT-5.5 guide: "GPT-5.5 is strongest when the prompt defines the target outcome, success criteria, constraints, and available context, then lets the model choose the path."

Both labs converge on the same shape. Name the destination. Name the path. Skip the prohibitions.

Layer 1 — The outcome block

Every non-trivial prompt opens with this: