The best results come from clear, specific instructions that remove guesswork. Treat your input like a mini-brief: define what you want, who it’s for, and what “done” looks like. When the model understands the goal, constraints, and context, the response becomes more accurate, consistent, and usable.
State the outcome in one sentence, using an action verb (draft, compare, summarize, generate options, rewrite). If there are multiple tasks, list them as numbered steps so nothing gets skipped.
Include the audience, scenario, and any background details the model can’t assume. For example: industry, product type, customer sophistication, region (U.S. vs. global), and what has already been tried. Context reduces generic output.
Good instructions specify constraints such as length, tone, reading level, required inclusions, and exclusions. If accuracy matters, require the model to flag uncertainty and ask clarifying questions before proceeding when needed.
Define the structure: bullet points, table, step-by-step list, JSON, or sections with headings. Formatting guidance is one of the fastest ways to make outputs consistently scannable and ready to use.
If you have a preferred style, paste a short sample and say “match this style.” If there are must-use facts, include them directly. When relevant, provide a small set of “good vs. bad” examples so the model can mirror what you consider acceptable.
Instead of asking for a complete redo, request specific edits: “tighten the intro,” “add three alternatives for the headline,” or “remove repetition in section two.” Small, precise adjustments outperform vague feedback.
For more detailed guidance and practical templates, visit https://candorale.com/how-to-create-the-best-ai-prompts/.
Reuse a stable template that includes the goal, audience, constraints, and required format. Keep inputs deterministic by supplying the same context and examples each time, then refine with narrowly scoped edits.
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