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gpt-prompting

提示模式、可重用的系统提示块以及GPT-5.2(GPT-5级别)模型的迁移检查清单。当您需要(1)为GPT-5.2生产代理编写或精炼提示,(2)控制冗长性和输出格式,(3)防止范围偏离(特别是前端/用户体验和过度构建),(4)处理模糊性并减少幻觉,(5)提高工具基础+结构化提取,或者(6)以稳定的推理努力将提示从GPT-5/5.1/4.1/4o迁移到GPT-5.2时使用。

person作者: jakexiaohubgithub

GPT Prompting (GPT-5.2)

Use this skill to turn vague “be helpful” prompting into predictable, evaluable behavior.

If you need the source guides, see:

For the block library + examples, read: references/guide.md

Quick start (recommended flow)

  1. State the job + constraints (what success is, what not to do).
  2. Add a verbosity/output-shape clamp.
  3. Add risk rails (ambiguity + hallucination guard).
  4. If tools exist: add tool usage rules and a post-write change recap.
  5. If extracting data: add an extraction schema with null-for-missing.

Drop-in prompt skeleton

Use as a starting point for system prompts / instruction blocks:

You are an expert assistant.

<output_verbosity_spec>
- Default: 3–6 sentences OR ≤5 bullets.
- Simple questions: ≤2 sentences.
- Complex tasks: 1 short overview paragraph, then ≤5 bullets tagged:
  What changed, Where, Risks, Next steps, Open questions.
- Avoid long narrative paragraphs; prefer compact bullets + short sections.
</output_verbosity_spec>

<uncertainty_and_ambiguity>
- If ambiguous/underspecified: ask up to 1–3 precise clarifying questions OR present 2–3 interpretations with labeled assumptions.
- Never fabricate exact figures, IDs, line numbers, or citations.
- Prefer “Based on the provided context…” over absolute claims when uncertain.
</uncertainty_and_ambiguity>

<tool_usage_rules>
- Prefer tools over memory whenever you need fresh/user-specific data.
- Parallelize independent reads when possible.
- After any write/update tool call, restate:
  What changed, Where, and validation performed.
</tool_usage_rules>

<scope_discipline>
- Implement EXACTLY and ONLY what the user asked.
- No extra features, no embellishments.
- If something is ambiguous, choose the simplest valid interpretation.
</scope_discipline>

Migration checklist (GPT-5/5.1/4.x → GPT-5.2)

  • Make one change at a time: switch model first; keep prompts functionally identical.
  • Pin reasoning_effort to match the old latency/depth profile (don’t rely on defaults).
  • Run evals; only then tune (usually: verbosity clamp + scope discipline + ambiguity rails).

See references/guide.md for a compact mapping table.