research.jkca.me

Prompts behave like physical systems under constraint. Temperature controls entropy. Context windows create artificial time horizons. Token limits enforce hard boundaries on complexity.

These constraints interact non-linearly. A tight token budget amplifies temperature effects. A wide context window can mask poor prompt structure.

Understanding these dynamics transforms prompting from guesswork into engineering. You can predict where outputs will diverge, compress, or collapse.

This note maps the observable physics of prompt behavior across constraint dimensions.

You can't make a language model deterministic by setting temperature to zero. You can only narrow the output distribution. True determinism requires architectural constraints, not just parameter tweaks.

The goal isn't identical outputs every time—it's predictable variance. Control the degrees of freedom, and randomness becomes a feature, not a bug.

Determinism emerges from structure: strict formats, explicit validation, and compositional prompts that isolate non-deterministic steps.

This note documents how to build deterministic systems on top of probabilistic foundations.

Manual review doesn't scale. If you're reading every AI output to check correctness, you're not using AI—you're using a very expensive draft generator.

Verification must be automated, layered, and probabilistic. You can't guarantee correctness, but you can bound error rates and detect drift.

The key is designing verification strategies that are cheaper than the task itself while maintaining acceptable false-negative rates.

This note catalogs practical verification patterns for production AI systems.

Then: I asked for everything at once. Long prompts, multiple tasks, vague goals. I thought more tokens meant better results. The model thrashed, I got inconsistent outputs, and I blamed the model.

Now: I ask for one thing, precisely. Single-purpose prompts, explicit formats, clear success criteria. Compression improves fidelity.

This shift isn't about "prompt engineering tricks." It's about respecting the model's operational constraints and designing around them.

This note documents the phase transition from kitchen-sink prompting to surgical prompting.