effective-prompting

Effective prompting

A prompt is a spec. Most "the model got it wrong" problems are underspecified prompts. Write the prompt the way you'd brief a sharp contractor who can't ask follow-up questions.

Make it specific

• State the task, inputs, constraints, and the exact output shape explicitly. Don't make the model guess the format or the success criteria.
• Add a role or context only when it changes behavior ("you are a security reviewer"), not as decoration.
• Front-load the stable instructions; put the volatile input (the actual data/question) last. It reads clearly and plays well with prompt caching.

Show, don't just tell

• Give a few examples for anything format- or judgment-sensitive — especially edge cases and the tricky "don't do this" cases. Examples beat adjectives.
• For machine-consumed output, specify a schema and ask for structured output (JSON or a tool/function call), then validate it — don't regex prose.

Give room to reason

• For multi-step tasks, ask the model to work through it before the final answer, and separate the reasoning from the answer with a delimiter you can parse. Don't force answer-first and then complain about quality.
• Decompose genuinely complex tasks into steps or separate calls rather than one mega-prompt.

Iterate against real failures

• Collect actual failing inputs, then add a targeted instruction or example for each. Don't pile on prophylactic rules for failures you haven't seen — over-constraining hurts quality.

Anti-patterns to flag

• Vague asks; contradictory instructions; the real task buried under preamble.
• Demanding a format with no example; "be creative but exact"; asking for reasoning then parsing the whole blob as the answer.
• Stuffing irrelevant context "just in case" — it dilutes attention and costs tokens.

Out of scope: model selection, temperature/thinking/effort knobs, token limits, and exact API parameters are provider-specific — consult your provider's docs. This skill is prompt craft.