refine-prompt

Refining Prompts

Process

1. Assess -- Identify what the prompt is missing:

1. Rewrite -- Transform into specification language: precise, imperative, no filler. Treat the prompt as a spec, not conversation.

2. Validate -- Check the rewrite against the assessment table. Every gap identified in step 1 must be addressed.

Rules

• Length: 0.75x–1.5x the original. Conciseness is a feature -- add only what's missing, cut what's vague.
• A line must change behavior. "Cut what's vague" and "cut what the model already does" are different filters, and the second removes far more -- every line reads as non-vague once it is imperative. If the model would act that way by default, delete the whole sentence rather than trimming words from it. The recurring offender is encouragement it already follows: "be careful", "be thorough", "think it through", "make sure to".
• Name the concept, don't explain it. Use terms the model knows (idempotent, invariant, race condition, TOCTOU, YAGNI) instead of spelling them out. Spell out only terms the project invented, once, in one place.
• State a rule once. If the same rule appears in two sections, cut one and point to the other.
• Pair every prohibition with the positive target. Steering by ban drags the forbidden behavior into context and makes it more available, not less -- the negation is a weak modifier riding on a strongly activated concept. Prompt the target instead ("write one-line comments" rather than "don't write long comments") so the banned behavior is never named. A bare prohibition earns its place only as a hard guardrail whose whole content is the refusal, with no behavior to substitute. Everywhere else the check is mechanical: every never and don't line states its replacement behavior.
• Never invent -- only use information present in the original prompt or conversation context. If critical info is missing, ask instead of assuming.
• Instruction hierarchy -- order sections by priority: task → constraints → examples → input data → output format. Place the most important instruction first.
• Progressive complexity -- start with the simplest prompt that could work. Add few-shot examples, chain-of-thought, or role framing only when the task demands it, not by default.
• Specific verbs -- replace vague actions ("analyze", "process", "handle") with measurable ones ("list the top 3", "classify as A/B/C", "return JSON with keys X, Y").
• One output format -- specify exactly one format (JSON schema, markdown template, numbered list). Ambiguous format expectations cause inconsistent results.
• Give the reason, not just the rule. A dense block of MUST/CRITICAL anchors the model on the instruction at the expense of the context it applies to, and bare imperatives compete rather than compound. Keep them few and motivated: state what the rule prevents in the same sentence, so the model generalizes to the case the rule did not name.
• No meta-commentary -- output only the refined prompt as markdown. No preamble ("Here's an improved version..."), no explanation of changes unless explicitly requested.