Prompt Writing Fundamentals
Master the five dimensions that separate pass-level outputs from reject-level ones. AI companies evaluate on specificity, context, role clarity, constraint definition, and output format. Learn to hit all five — consistently.
Clarity
Ambiguous prompts produce ambiguous outputs. The evaluator's rubric penalizes vague language — words like "good," "nice," "some," "a few." These don't constrain the model, so it fills the gap with generic filler.
A clear prompt uses concrete terms: numbers instead of quantities, specific names instead of categories, exact behaviors instead of general directions.
Specificity
Broad prompts generate surface-level outputs. Evaluators are calibrated to catch outputs that demonstrate "obvious coverage" without deep execution. The gap between a pass and a reject is usually 2-3 levels of specificity you didn't include.
Specificity means: naming the exact format, the exact scope, the exact edge cases, the exact audience's knowledge level, and the exact deliverable structure — before the model starts writing.
Context
The same instruction lands differently depending on what the model knows about the situation. Without context, models default to generic — safe, shallow, broadly applicable. Evaluators test whether you can give the model enough situation to write as if it already knows what's going on.
Context includes: background information, intended use case, prior decisions already made, constraints the reader won't know, and the stakes of getting it wrong.
Role Definition
Role definitions aren't decorative — they set the model's decision-making framework. The same task done "as a junior copywriter" vs "as a CRO strategist" produces categorically different outputs. Evaluators assign separate rubric scores for whether you correctly invoked the right persona.
A good role definition has three parts: the persona (who it is), the perspective (how it thinks), and the boundary (what it won't do or say).
Output Formatting
Unstructured outputs get rejected. Evaluators score whether the output matches the format specified in the prompt. The bar isn't "any structure" — it's the structure that actually serves the use case. Bullets where paragraphs are needed is as bad as paragraphs where bullets are needed.
Format specifications include: structure type (paragraph, bullet, table, JSON, script), length constraints, section ordering, header presence, and any mandatory fields that must appear.
Prompt Scorer
Paste a prompt you've written and get scored on the five dimensions evaluators care about. This simulates the rubric-based scoring used in actual AI company assessments.
Practice Exercises
Review each AI output and score it using the rubric. Then compare against the evaluator's reasoning. These are drawn from real assessment scenarios.
Score this output on each dimension. Select a score (1-10) for each, then compare against the evaluator's verdict below.
The Support Team
Score this output on each dimension.
Score this output on each dimension.