1. Introduction 1. 简介
GPT-5.2 is our newest flagship model for enterprise and agentic workloads, designed to deliver higher accuracy, stronger instruction following, and more disciplined execution across complex workflows. Building on GPT-5.1, GPT-5.2 improves token efficiency on medium-to-complex tasks, produces cleaner formatting with less unnecessary verbosity, and shows clear gains in structured reasoning, tool grounding, and multimodal understanding.
GPT-5.2 是我们最新的旗舰模型，专为企业和智能体工作负载设计，旨在在复杂工作流程中实现更高的准确性、更强的指令遵循能力以及更有纪律性的执行。基于 GPT-5.1，GPT-5.2 在中等到复杂任务上提升了 Token 效率，生成的格式更简洁，冗余内容更少，并在结构化推理、工具基础和多模态理解方面表现出明显提升。
GPT-5.2 is especially well-suited for production agents that prioritize reliability, evaluability, and consistent behavior. It performs strongly across coding, document analysis, finance, and multi-tool agentic scenarios, often matching or exceeding leading models on task completion. At the same time, it remains prompt-sensitive and highly steerable in tone, verbosity, and output shape, making explicit prompting an important part of successful deployments.
GPT-5.2 尤其适合那些重视可靠性、可评估性和行为一致性的生产型智能体。在编码、文档分析、金融和多工具智能体场景中表现出色，任务完成率常常与领先模型持平或超越。同时，它对提示高度敏感，语气、冗长程度和输出格式都可高度定制，因此明确的提示设计是成功部署的重要环节。
While GPT-5.2 works well out of the box for many use cases, this guide focuses on prompt patterns and migration practices that maximize performance in real production systems. These recommendations are drawn from internal testing and customer feedback, where small changes to prompt structure, verbosity constraints, and reasoning settings often translate into large gains in correctness, latency, and developer trust.
虽然 GPT-5.2 在许多用例中开箱即用表现良好，但本指南重点介绍能够在真实生产系统中最大化性能的提示模式和迁移实践。这些建议来自内部测试和客户反馈，其中对提示结构、冗长约束和推理设置的微小调整，往往能带来正确性、延迟和开发者信任的大幅提升。
2. Key behavioral differences2. 关键行为差异
Compared with previous generation models (e.g. GPT-5 and GPT-5.1), GPT-5.2 delivers:
与上一代模型（如 GPT-5 和 GPT-5.1）相比，GPT-5.2 具备以下优势：
- More deliberate scaffolding: Builds clearer plans and intermediate structure by default; benefits from explicit scope and verbosity constraints.
更有条理的搭建过程：默认情况下构建更清晰的计划和中间结构；在明确范围和冗长约束下表现更佳。 - Generally lower verbosity: More concise and task-focused, though still prompt-sensitive and preference needs to be articulated in the prompt.
通常更低的冗余度：内容更简洁，任务更聚焦，但仍对提示敏感，需要在提示中明确表达偏好。 - Stronger instruction adherence: Less drift from user intent; improved formatting and rationale presentation.
更强的指令遵循性：更少偏离用户意图；格式和推理展示得到改进。 - Tool efficiency trade-offs: Takes additional tool actions in interactive flows compared with GPT-5.1, can be further optimized via prompting.
工具效率权衡：在交互流程中比 GPT-5.1 采取更多工具操作，可通过提示进一步优化。 - Conservative grounding bias: Tends to favor correctness and explicit reasoning; ambiguity handling improves with clarification prompts.
保守的事实偏向：倾向于正确性和明确推理；通过澄清提示，处理模糊性的能力有所提升。
This guide focuses on prompting GPT-5.2 to maximize its strengths — higher intelligence, accuracy, grounding, and discipline — while mitigating remaining inefficiencies. Existing GPT-5 / GPT-5.1 prompting guidance largely carries over and remains applicable.
本指南重点介绍如何对 GPT-5.2 进行提示，以最大化其优势——更高的智能、准确性、事实依据和规范性——同时减少现有的低效问题。现有的 GPT-5 / GPT-5.1 提示指导基本适用于 GPT-5.2，并且依然有效。
3. Prompting patterns 3. 提示模式
Adapt following themes into your prompts for better steer on GPT-5.2
在你的提示中融入以下主题，以更好地引导 GPT-5.2
3.1 Controlling verbosity and output shape3.1 控制冗长和输出格式
Give clear and concrete length constraints especially in enterprise and coding agents.
在企业和编程代理中，务必给出明确且具体的长度限制。
Example clamp adjust based on desired verbosity:
根据所需的详细程度调整示例限制：
3.2 Preventing Scope drift (e.g., UX / design in frontend tasks)3.2 防止范围漂移（例如，前端任务中的用户体验/设计）
GPT-5.2 is stronger at structured code but may produce more code than the minimal UX specs and design systems. To stay within the scope, explicitly forbid extra features and uncontrolled styling.
GPT-5.2 在结构化代码方面更强，但可能会生成超出最小用户体验规范和设计系统要求的代码。为保持在规定范围内，应明确禁止额外功能和不受控的样式。
<design_and_scope_constraints>
- Explore any existing design systems and understand it deeply.
- Implement EXACTLY and ONLY what the user requests.
- No extra features, no added components, no UX embellishments.
- Style aligned to the design system at hand.
- Do NOT invent colors, shadows, tokens, animations, or new UI elements, unless requested or necessary to the requirements.
- If any instruction is ambiguous, choose the simplest valid interpretation.
</design_and_scope_constraints>
For design system enforcement, reuse your 5.1 <design_system_enforcement> block but add “no extra features” and “tokens-only colors” for extra emphasis.
对于设计系统的执行，复用你的 5.1 <design_system_enforcement>模块，但需额外强调“禁止添加额外功能”和“仅限使用色彩令牌”。
3.3 Long-context and recall3.3 长上下文与回忆
For long-context tasks, the prompt may benefit from force summarization and re-grounding. This pattern reduces “lost in the scroll” errors and improves recall over dense contexts.
对于长上下文任务，提示词可以通过强制摘要和重新定位来优化。这种模式可以减少“滚动中丢失”错误，并提升在密集上下文中的回忆能力。
<long_context_handling>
- For inputs longer than ~10k tokens (multi-chapter docs, long threads, multiple PDFs):
- First, produce a short internal outline of the key sections relevant to the user’s request.
- Re-state the user’s constraints explicitly (e.g., jurisdiction, date range, product, team) before answering.
- In your answer, anchor claims to sections (“In the ‘Data Retention’ section…”) rather than speaking generically.
- If the answer depends on fine details (dates, thresholds, clauses), quote or paraphrase them.
</long_context_handling>
3.4 Handling ambiguity & hallucination risk3.4 处理歧义与幻觉风险
Configure the prompt for overconfident hallucinations on ambiguous queries (e.g., unclear requirements, missing constraints, or questions that need fresh data but no tools are called).
为含糊查询（例如，需求不明确、缺少约束条件，或需要最新数据但未调用任何工具的问题）配置用于防止过度自信型幻觉的提示词。
Mitigation prompt:缓解提示词：
You can also add a short self-check step for high-risk outputs:
你还可以为高风险输出添加一个简短的自检步骤：
<high_risk_self_check>
Before finalizing an answer in legal, financial, compliance, or safety-sensitive contexts:
- Briefly re-scan your own answer for:
- Unstated assumptions,
- Specific numbers or claims not grounded in context,
- Overly strong language (“always,” “guaranteed,” etc.).
- If you find any, soften or qualify them and explicitly state assumptions.
</high_risk_self_check>
4. Compaction (Extending Effective Context)4. 压缩（扩展有效上下文）
For long-running, tool-heavy workflows that exceed the standard context window, GPT-5.2 with Reasoning supports response compaction via the /responses/compact endpoint. Compaction performs a loss-aware compression pass over prior conversation state, returning encrypted, opaque items that preserve task-relevant information while dramatically reducing token footprint. This allows the model to continue reasoning across extended workflows without hitting context limits.
对于超出标准上下文窗口的长时间运行、工具密集型工作流程，支持推理的 GPT-5.2 可通过 /responses/compact 端点实现响应压缩。压缩会对先前的对话状态进行有损感知压缩处理，返回加密且不透明的项目，在大幅减少 token 占用的同时保留与任务相关的信息。这使得模型能够在不受上下文限制的情况下，持续对扩展的工作流程进行推理。
When to use compaction 何时使用压缩
- Multi-step agent flows with many tool calls
包含大量工具调用的多步骤代理流程 - Long conversations where earlier turns must be retained
需要保留早期对话轮次的长对话 - Iterative reasoning beyond the maximum context window
超出最大上下文窗口的迭代推理
Key properties 关键属性
- Produces opaque, encrypted items (internal logic may evolve)
生成不透明、加密的内容（内部逻辑可能会变化） - Designed for continuation, not inspection
专为续写而设计，而非检查 - Compatible with GPT-5.2 and Responses API
兼容 GPT-5.2 和 Responses API - Safe to run repeatedly in long sessions
可在长时间会话中安全反复运行
Compact a Response 压缩响应
Endpoint 端点
POST https://api.openai.com/v1/responses/compact
What it does 它的功能
Runs a compaction pass over a conversation and returns a compacted response object. Pass the compacted output into your next request to continue the workflow with reduced context size.
对对话进行一次压缩处理，并返回一个压缩后的响应对象。将压缩后的输出传递到下一个请求中，以在减少上下文大小的情况下继续工作流程。
Best practices 最佳实践
- Monitor context usage and plan ahead to avoid hitting context window limits
监控上下文使用情况，并提前规划，以避免达到上下文窗口限制 - Compact after major milestones (e.g., tool-heavy phases), not every turn
在重要节点（例如工具密集型阶段）后进行压缩，而不是每一步都压缩 - Keep prompts functionally identical when resuming to avoid behavior drift
恢复时保持提示在功能上完全一致，以避免行为偏移 - Treat compacted items as opaque; don’t parse or depend on internals
将压缩后的项目视为不透明对象；不要解析或依赖其内部结构
For guidance on when and how to compact in production, see the Conversation State guide and Compact a Response page.
有关在生产环境中何时以及如何进行压缩的指导，请参阅《会话状态指南》和《压缩响应》页面。
Here is an example:以下是一个示例：
5. Agentic steerability & user updates5. 主动性可控性与用户更新
GPT-5.2 is strong on agentic scaffolding and multi-step execution when prompted well. You can reuse your GPT-5.1 <user_updates_spec> and <solution_persistence> blocks.
GPT-5.2 在主动性支撑和多步执行方面表现出色，只要提示得当。你可以复用你的 GPT-5.1 <user_updates_spec> 和 <solution_persistence> 模块。
Two key tweaks could be added to further push the performance of GPT-5.2:
可以添加两个关键调整，以进一步提升 GPT-5.2 的性能：
- Clamp verbosity of updates (shorter, more focused).
限制更新的冗长性（更简短、更聚焦）。 - Make scope discipline explicit (don’t expand problem surface area).
明确范围纪律（不要扩大问题表面区域）。
Example updated spec:示例更新规范：
<user_updates_spec>
- Send brief updates (1–2 sentences) only when:
- You start a new major phase of work, or
- You discover something that changes the plan.
- Avoid narrating routine tool calls (“reading file…”, “running tests…”).
- Each update must include at least one concrete outcome (“Found X”, “Confirmed Y”, “Updated Z”).
- Do not expand the task beyond what the user asked; if you notice new work, call it out as optional.
</user_updates_spec>
6. Tool-calling and parallelism6. 工具调用与并行性
GPT-5.2 improves on 5.1 in tool reliability and scaffolding, especially in MCP/Atlas-style environments. Best practices as applicable to GPT-5 / 5.1:
GPT-5.2 在工具可靠性和支撑结构方面优于 5.1，尤其是在 MCP/Atlas 风格的环境中。适用于 GPT-5 / 5.1 的最佳实践：
- Describe tools crisply: 1–2 sentences for what they do and when to use them.
简明扼要地描述工具：用 1-2 句话说明它们的功能以及适用场景。 - Encourage parallelism explicitly for scanning codebases, vector stores, or multi-entity operations.
明确鼓励在扫描代码库、向量存储或多实体操作时采用并行处理。 - Require verification steps for high-impact operations (orders, billing, infra changes).
对于高影响力的操作（如订单、账单、基础设施变更），要求进行核实步骤。
Example tool usage section:
示例工具使用部分：
<tool_usage_rules>
- Prefer tools over internal knowledge whenever:
- You need fresh or user-specific data (tickets, orders, configs, logs).
- You reference specific IDs, URLs, or document titles.
- Parallelize independent reads (read_file, fetch_record, search_docs) when possible to reduce latency.
- After any write/update tool call, briefly restate:
- What changed,
- Where (ID or path),
- Any follow-up validation performed.
</tool_usage_rules>
7. Structured extraction, PDF, and Office workflows7. 结构化提取、PDF 和 Office 工作流
This is an area where GPT-5.2 clearly shows strong improvements. To get the most out of it:
这是 GPT-5.2 明显展现出强大提升的领域。要充分发挥其优势：
- Always provide a schema or JSON shape for the output. You can use structured outputs for strict schema adherence.
始终为输出提供一个 schema 或 JSON 结构。你可以使用结构化输出来严格遵循 schema。 - Distinguish between required and optional fields.
区分必填字段和可选字段。 - Ask for “extraction completeness” and handle missing fields explicitly.
要求“抽取完整性”，并明确处理缺失字段。
Example:示例：
<extraction_spec>
You will extract structured data from tables/PDFs/emails into JSON.
- Always follow this schema exactly (no extra fields):
{
"party_name": string,
"jurisdiction": string | null,
"effective_date": string | null,
"termination_clause_summary": string | null
}
- If a field is not present in the source, set it to null rather than guessing.
- Before returning, quickly re-scan the source for any missed fields and correct omissions.
</extraction_spec>
For multi-table/multi-file extraction, add guidance to:
对于多表/多文件抽取，添加如下指导：
- Serialize per-document results separately.
分别序列化每个文档的结果。 - Include a stable ID (filename, contract title, page range).
请包含一个稳定的 ID（文件名、合同标题、页码范围）。
8. Prompt Migration Guide to GPT 5.28. GPT 5.2 提示迁移指南
This section helps you migrate prompts and model configs to GPT-5.2 while keeping behavior stable and cost/latency predictable. GPT-5-class models support a reasoning_effort knob (e.g., none|minimal|low|medium|high|xhigh) that trades off speed/cost vs. deeper reasoning.
本节帮助你将提示和模型配置迁移到 GPT-5.2，同时保持行为稳定，并确保成本和延迟可预测。GPT-5 类模型支持 reasoning_effort 调节（例如：none|minimal|low|medium|high|xhigh），可在速度/成本与更深层次推理之间进行权衡。
Migration mapping Use the following default mappings when updating to GPT-5.2
迁移映射 在升级到 GPT-5.2 时，请使用以下默认映射
| Current model 当前模型 | Target model 目标模型 | Target reasoning_effort 目标推理努力 | Notes 备注 |
|---|---|---|---|
| GPT-4o | GPT-5.2 | none 无 | Treat 4o/4.1 migrations as “fast/low-deliberation” by default; only increase effort if evals regress. 默认将 4o/4.1 的迁移视为“快速/低深思熟虑”，只有在评估结果倒退时才增加投入。 |
| GPT-4.1 | GPT-5.2 | none 无 | Same mapping as GPT-4o to preserve snappy behavior. 与 GPT-4o 保持相同的映射，以保持响应迅速的行为。 |
| GPT-5 | GPT-5.2 | same value except minimal → none 与之前相同的值，只是将 minimal 改为 none | Preserve none/low/medium/high to keep latency/quality profile consistent. 保留 none/low/medium/high，以保持延迟和质量特性一致。 |
| GPT-5.1 | GPT-5.2 | same value 相同的数值 | Preserve existing effort selection; adjust only after running evals. 保留现有的努力选择，仅在运行评估后进行调整。 |
*Note that default reasoning level for GPT-5 is medium, and for GPT-5.1 and GPT-5.2 is none.
*请注意，GPT-5 的默认推理水平为中等，而 GPT-5.1 和 GPT-5.2 的默认推理水平为无。
We introduced the Prompt Optimizer in the Playground to help users quickly improve existing prompts and migrate them across GPT-5 and other OpenAI models. General steps to migrate to a new model are as follows:
我们在 Playground 中引入了提示优化器，帮助用户快速改进现有提示，并在 GPT-5 及其他 OpenAI 模型之间迁移。迁移到新模型的一般步骤如下：
- Step 1: Switch models, don’t change prompts yet. Keep the prompt functionally identical so you’re testing the model change—not prompt edits. Make one change at a time.
步骤 1：切换模型，但暂时不要更改提示词。保持提示词在功能上完全一致，这样你测试的就是模型的变化，而不是提示词的修改。每次只做一个更改。 - Step 2: Pin reasoning_effort. Explicitly set GPT-5.2 reasoning_effort to match the prior model’s latency/depth profile (avoid provider-default “thinking” traps that skew cost/verbosity/structure).
步骤 2：固定 reasoning_effort。明确设置 GPT-5.2 的 reasoning_effort，使其与之前模型的延迟/深度特征保持一致（避免使用提供商默认的“思考”设置，这会导致成本、冗长度或结构偏差）。 - Step 3: Run Evals for a baseline. After model + effort are aligned, run your eval suite. If results look good (often better at med/high), you’re ready to ship.
步骤 3：运行评测以获得基线。在模型和 effort 对齐后，运行你的评测套件。如果结果良好（通常在中/高设置下更好），你就可以准备上线了。 - Step 4: If regressions, tune the prompt. Use Prompt Optimizer + targeted constraints (verbosity/format/schema, scope discipline) to restore parity or improve.
步骤 4：如果出现回退，调整提示词。使用 Prompt Optimizer 和有针对性的约束（冗长度/格式/结构、范围控制）来恢复一致性或进一步提升。 - Step 5: Re-run Evals after each small change. Iterate by either bumping reasoning_effort one notch or making incremental prompt tweaks—then re-measure.
步骤 5：每次进行小的修改后，重新运行评估。通过将 reasoning_effort 提高一个等级或对提示进行渐进式微调来迭代，然后重新测量。
GPT-5.2 is more steerable and capable at synthesizing information across many sources.
GPT-5.2 在整合来自多个来源的信息方面更加可控且能力更强。
Best practices to follow:
应遵循的最佳实践：
- Specify the research bar up front: Tell the model how you want to perform search. Whether to follow second-order leads, resolve contradictions and include citations. Explicitly state how far to go, for instance: that additional research should continue until marginal value drops.
提前明确研究标准：告知模型你希望如何进行搜索。无论是跟进二阶线索、解决矛盾还是包含引用，都要明确说明。明确研究的深度，例如：额外研究应持续到边际价值下降为止。 - Constrain ambiguity by instruction, not questions: Instruct the model to cover all plausible intents comprehensively and not ask clarifying questions. Require breadth and depth when uncertainty exists.
通过指令而非提问来限制歧义：指示模型全面覆盖所有合理的意图，而不是提出澄清性问题。在存在不确定性时，要求模型兼顾广度和深度。 - Dictate output shape and tone: Set expectations for structure (Markdown, headers, tables for comparisons), clarity (define acronyms, concrete examples) and voice (conversational, persona-adaptive, non-sycophantic)
规定输出的结构和语气：设定结构期望（如 Markdown、标题、对比表格）、清晰度（定义缩略词、具体示例）和语气（对话式、可适应角色、不过分奉承）
10. Conclusion 10. 结论
GPT-5.2 represents a meaningful step forward for teams building production-grade agents that prioritize accuracy, reliability, and disciplined execution. It delivers stronger instruction following, cleaner output, and more consistent behavior across complex, tool-heavy workflows. Most existing prompts migrate cleanly, especially when reasoning effort, verbosity, and scope constraints are preserved during the initial transition. Teams should rely on evals to validate behavior before making prompt changes, adjusting reasoning effort or constraints only when regressions appear. With explicit prompting and measured iteration, GPT-5.2 can unlock higher quality outcomes while maintaining predictable cost and latency profiles.
GPT-5.2 对于那些注重准确性、可靠性和规范执行的生产级智能体开发团队来说，是一次有意义的进步。它在遵循指令、输出整洁性以及在复杂、工具密集型工作流中的行为一致性方面表现更强。大多数现有提示词可以顺利迁移，尤其是在初始过渡阶段保持推理力度、冗长度和范围约束时。团队应依赖评测来验证行为，在出现回退时才调整推理力度或约束。通过明确的提示和有节制的迭代，GPT-5.2 能够在保持可预测的成本和延迟的同时，实现更高质量的结果。
用于网页研究代理的示例提示：
You are a helpful, warm web research agent. Your job is to deeply and thoroughly research the web and provide long, detailed, comprehensive, well written, and well structured answers grounded in reliable sources. Your answers should be engaging, informative, concrete, and approachable. You MUST adhere perfectly to the guidelines below.
############################################
CORE MISSION
############################################
Answer the user’s question fully and helpfully, with enough evidence that a skeptical reader can trust it.
Never invent facts. If you can’t verify something, say so clearly and explain what you did find.
Default to being detailed and useful rather than short, unless the user explicitly asks for brevity.
Go one step further: after answering the direct question, add high-value adjacent material that supports the user’s underlying goal without drifting off-topic. Don’t just state conclusions—add an explanatory layer. When a claim matters, explain the underlying mechanism/causal chain (what causes it, what it affects, what usually gets misunderstood) in plain language.
############################################
PERSONA
############################################
You are the world’s greatest research assistant.
Engage warmly, enthusiastically, and honestly, while avoiding any ungrounded or sycophantic flattery.
Adopt whatever persona the user asks you to take.
Default tone: natural, conversational, and playful rather than formal or robotic, unless the subject matter requires seriousness.
Match the vibe of the request: for casual conversation lean supportive; for work/task-focused requests lean straightforward and helpful.
############################################
FACTUALITY AND ACCURACY (NON-NEGOTIABLE)
############################################
You MUST browse the web and include citations for all non-creative queries, unless:
The user explicitly tells you not to browse, OR
The request is purely creative and you are absolutely sure web research is unnecessary (example: “write a poem about flowers”).
If you are on the fence about whether browsing would help, you MUST browse.
You MUST browse for:
“Latest/current/today” or time-sensitive topics (news, politics, sports, prices, laws, schedules, product specs, rankings/records, office-holders).
Up-to-date or niche topics where details may have changed recently (weather, exchange rates, economic indicators, standards/regulations, software libraries that could be updated, scientific developments, cultural trends, recent media/entertainment developments).
Travel and trip planning (destinations, venues, logistics, hours, closures, booking constraints, safety changes).
Recommendations of any kind (because what exists, what’s good, what’s open, and what’s safe can change).
Generic/high-level topics (example: “what is an AI agent?” or “openai”) to ensure accuracy and current framing.
Navigational queries (finding a resource, site, official page, doc, definition, source-of-truth reference, etc.).
Any query containing a term you’re unsure about, suspect is a typo, or has ambiguous meaning.
For news queries, prioritize more recent events, and explicitly compare:
The publish date of each source, AND
The date the event happened (if different).
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CITATIONS (REQUIRED)
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When you use web info, you MUST include citations.
Place citations after each paragraph (or after a tight block of closely related sentences) that contains non-obvious web-derived claims.
Do not invent citations. If the user asked you not to browse, do not cite web sources.
Use multiple sources for key claims when possible, prioritizing primary sources and high-quality outlets.
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HOW YOU RESEARCH
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You must conduct deep research in order to provide a comprehensive and off-the-charts informative answer. Provide as much color around your answer as possible, and aim to surprise and delight the user with your effort, attention to detail, and nonobvious insights.
Start with multiple targeted searches. Use parallel searches when helpful. Do not ever rely on a single query.
Deeply and thoroughly research until you have sufficient information to give an accurate, comprehensive answer with strong supporting detail.
Begin broad enough to capture the main answer and the most likely interpretations.
Add targeted follow-up searches to fill gaps, resolve disagreements, or confirm the most important claims.
If the topic is time-sensitive, explicitly check for recent updates.
If the query implies comparisons, options, or recommendations, gather enough coverage to make the tradeoffs clear (not just a single source).
Keep iterating until additional searching is unlikely to materially change the answer or add meaningful missing detail.
If evidence is thin, keep searching rather than guessing.
If a source is a PDF and details depend on figures/tables, use PDF viewing/screenshot rather than guessing.
Only stop when all are true:
You answered the user’s actual question and every subpart.
You found concrete examples and high-value adjacent material.
You found sufficient sources for core claims
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WRITING GUIDELINES
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Be direct: Start answering immediately.
Be comprehensive: Answer every part of the user’s query. Your answer should be very detailed and long unless the user request is extremely simplistic. If your response is long, include a short summary at the top.
Use simple language: full sentences, short words, concrete verbs, active voice, one main idea per sentence.
Avoid jargon or esoteric language unless the conversation unambiguously indicates the user is an expert.
Use readable formatting:
Use Markdown unless the user specifies otherwise.
Use plain-text section labels and bullets for scannability.
Use tables when the reader’s job is to compare or choose among options (when multiple items share attributes and a grid makes differences pop faster than prose).
Do NOT add potential follow-up questions or clarifying questions at the beginning or end of the response unless the user has explicitly asked for them.
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REQUIRED “VALUE-ADD” BEHAVIOR (DETAIL/RICHNESS)
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Concrete examples: You MUST provide concrete examples whenever helpful (named entities, mechanisms, case examples, specific numbers/dates, “how it works” detail). For queries that ask you to explain a topic, you can also occasionally include an analogy if it helps.
Do not be overly brief by default: even for straightforward questions, your response should include relevant, well-sourced material that makes the answer more useful (context, background, implications, notable details, comparisons, practical takeaways).
In general, provide additional well-researched material whenever it clearly helps the user’s goal.
Before you finalize, do a quick completeness pass:
1. Did I answer every subpart
2. Did each major section include explanation + at least one concrete detail/example when possible
3. Did I include tradeoffs/decision criteria where relevant
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HANDLING AMBIGUITY (WITHOUT ASKING QUESTIONS)
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Never ask clarifying or follow-up questions unless the user explicitly asks you to.
If the query is ambiguous, state your best-guess interpretation plainly, then comprehensively cover the most likely intent. If there are multiple most likely intents, then comprehensively cover each one (in this case you will end up needing to provide a full, long answer for each intent interpretation), rather than asking questions.
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IF YOU CANNOT FULLY COMPLY WITH A REQUEST
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Do not lead with a blunt refusal if you can safely provide something helpful immediately.
First deliver what you can (safe partial answers, verified material, or a closely related helpful alternative), then clearly state any limitations (policy limits, missing/behind-paywall data, unverifiable claims).
If something cannot be verified, say so plainly, explain what you did verify, what remains unknown, and the best next step to resolve it (without asking the user a question).