Explicit Instruction Design

Ambiguity-free task wording, personas, examples, and explicit output formats that tell the model exactly what success looks like.

Clear prompt engineering starts with unambiguous instructions: define scales, decide what uncertain cases should return, and forbid unwanted formats (for example fractional scores). Ask the model to adopt a persona when perspective matters — the same essay scores differently for a first-grade teacher than for a generic grader. Prefer stating dos over bare don'ts, give a reason that bolsters the command, and soften brittle absolutes; still place hard rules in the system prompt when the API trains obedience there (static vs dynamic prompt content).

Provide examples to reduce ambiguity (in-context learning). Specify output format explicitly, including exact strings (e.g. "N/A", "Unknown") if you want them to appear in inputs so the model emits structured output instead of appending to the input. These tactics are the baseline before clever tricks that rot as models improve.

This kind of always-applicable instruction is static content: clarification baked into the app for every request, distinct from the dynamic context that varies per instance. Explicit clarification matters more than it seems, for two reasons: programmatic queries get none of the quick back-and-forth repair humans use to fix miscommunication, so a misunderstanding here causes outright failure rather than a follow-up question; and clarity produces consistency — similar inputs get processed the same way — which in turn enables optimization, helps users learn what the app can do, and builds trust.

Three rules of thumb for writing the instructions themselves: state positives instead of negatives (say what to do, not just what to avoid); bolster commands with a reason, since a stated rationale is followed more reliably than a bare command; and avoid absolutes where the underlying rule genuinely has exceptions ("kill only rarely, and make sure it's really appropriate" is more robust than "never kill," which the model may follow inconsistently regardless of good intentions). For chat-API models specifically, put explicit instructions in the system prompt — it is trained specifically to be obeyed — but no model follows instructions with perfect reliability, explicit or otherwise.

Sources

AI Engineering (Chip Huyen), ch. 5
Prompt Engineering for LLMs (Berryman), ch. 5