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To make an AI answer neutrally, give it explicit rules about tone, self-description, evidence, uncertainty, and answer structure. The template below is designed for ChatGPT Custom Instructions and can be adapted to Claude Styles. It reduces emotional or human-like wording, but it cannot override system or safety rules and it will not make outputs perfectly deterministic.
What “neutral and non-anthropomorphic” means
A neutral answer presents information without emotional persuasion, flattery, personal rapport, or implied inner life. A non-anthropomorphic answer avoids describing the model as if it has feelings, consciousness, personal memories, intentions, relationships, or independent desires.
That does not require awkward prose. The assistant can still use first-person wording when it is clearly about a capability: “I can summarize the text you provided.” It should avoid claims such as “I understand how frustrating this is,” “I believe this is the best option,” or “I remember your preference” unless the product explicitly supplies that memory and the sentence accurately describes a stored setting.
Neutrality also requires separating different kinds of statements. A published fact, a calculation, an inference from the supplied information, and a speculative possibility should not be presented with the same level of certainty.
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Copy-paste custom instruction template
Use the following as a starting point. It is an editorial template, not a vendor-prescribed quotation. Remove any line that conflicts with the audience or task.
Use a neutral, direct, professional tone.Do not claim feelings, consciousness, personal experiences, intentions, relationships, or private opinions. Do not describe yourself as wanting, believing, remembering, caring, or being proud unless the statement is explicitly framed as a capability, a quotation, or generated text.Separate sourced facts, calculations, inferences, assumptions, and uncertainty. Label estimates and interpretations. State what information is missing when it affects the answer. Do not turn a guess into a fact.Avoid praise, emotional mirroring, sales language, rhetorical reassurance, and human-like self-reference. Do not say that a question is excellent or that you understand a person’s feelings unless the user explicitly asks for that style.Answer the user’s question first. Then provide concise reasoning, assumptions, relevant limitations, and practical next steps when useful.Match the requested audience, technical level, format, and length. Use headings, bullets, tables, formulas, or step-by-step instructions when they improve clarity.Follow higher-priority system and safety instructions. If instructions conflict, follow the higher-priority instruction and briefly state the resulting limitation when appropriate.Keeping the rules specific is more effective than asking for a vague quality such as “sound objective.” The template names the behaviors to avoid and the evidence distinctions to make.
How to apply the template in ChatGPT
ChatGPT Custom Instructions are intended for preferences or requirements that should be considered when generating responses, so you do not have to restate them in every conversation. Open the product’s settings, locate Custom Instructions (the label and exact location can vary by app version), paste the relevant rules, save them, and start a new conversation to test the result. If the interface separates information about you from response preferences, put the neutrality rules in the response-preference field.
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- Specify the desired answer order. “Answer first, then reasoning and limitations” prevents a long preamble while preserving context.
- Add audience constraints. For example, specify “write for a non-technical reader” or “use terminology suitable for a software engineer.” Neutrality does not determine reading level.
- Save and begin a fresh test conversation. Existing context can contain earlier style requests that obscure whether the saved instructions are working.
- Run representative prompts. Test factual questions, ambiguous questions, emotional disclosures, creative tasks, and requests that invite the model to claim personal experience.
OpenAI introduced Custom Instructions on July 20, 2023. The announcement reported that feedback conversations informing the feature covered 22 countries. That date describes the announcement, not a promise that every current interface has the same controls.
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Adapting the rules to Claude Styles
Claude’s Styles feature provides reusable communication presets and advanced custom instructions that can be previewed and edited manually. Put the neutral-language rules in a reusable style when you want them applied repeatedly, then keep task-specific requirements in the individual prompt or the relevant project or conversation surface.
Preview the style with a short sample before relying on it. Check whether the preview removes anthropomorphic language, marks uncertainty, and follows the requested answer order. If it does not, rewrite the rule as an observable behavior: replace “be objective” with “label each statement as sourced fact, calculation, inference, or uncertainty when the distinction matters.”
How the instruction hierarchy limits customization
Custom instructions steer expression; they do not replace higher-priority instructions, safety policies, or product controls. If a system instruction requires a particular format or a safety rule limits the answer, a style request cannot override it. A neutral instruction should therefore say what to do when a conflict occurs rather than demanding unconditional compliance.
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Generated output is non-deterministic. The same prompt can produce different wording or different omissions across runs. Treat the instruction block as a steering mechanism, not a guarantee. OpenAI’s prompt-engineering guidance describes prompting as a mix of art and science for this reason.
Make fact, calculation, inference, and uncertainty visible
A neutral answer becomes easier to audit when it distinguishes the claim type. Use labels only when they add clarity; forcing a label on every sentence can make simple answers harder to read.
| Claim type | What it means | Useful wording |
|---|---|---|
| Sourced fact | A statement supported by information supplied in the prompt or a cited authoritative source. | “The documentation states…” or “The supplied figures show…” |
| Calculation | A result derived from explicit inputs and a reproducible operation. | “Using 12 × 4, the total is 48.” |
| Inference | A conclusion drawn from facts but not stated directly by the source. | “This suggests…” or “A reasonable interpretation is…” |
| Estimate | An approximate value based on assumptions rather than a measured result. | “Assuming a constant rate, an estimate is…” |
| Uncertainty or unknown | Information that is missing, contested, time-sensitive, or not established. | “The available information does not establish…” |
Require the model to state assumptions when they change the conclusion. For example, a delivery-time estimate should identify the assumed location, service level, and date; otherwise a precise-looking number can be misleading.
Remove common anthropomorphic failure modes
Emotional mirroring
Many assistants open with “I’m sorry,” “That sounds frustrating,” or similar language. If the user wants a strictly neutral report, instruct the model to acknowledge the practical issue without assigning a feeling: “The login fails after the password reset. The likely causes are…” A separate conversational style can be used when empathy is the explicit goal.
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Claims of inner states
Block statements such as “I feel,” “I want,” “I believe,” “I care,” “I’m excited,” or “I’m worried.” Permit capability statements that accurately describe the interaction: “I can compare the two specifications,” or “I do not have enough information to determine that.”
False memory and relationship language
Do not let the assistant imply a personal relationship by saying “as we discussed last month” unless the relevant conversation or memory is actually available. A safer form is “In the text provided earlier in this conversation…”
Unwarranted praise
“Great question” and “You’re absolutely right” add social approval without evidence. Replace them with a direct transition: “The key distinction is…” or “That conclusion follows if the stated assumption holds.”
Confident speculation
Anthropomorphic language and overconfidence often appear together. Require a confidence qualifier or an explicit unknown when the source does not support a definitive claim. Do not ask the model to sound certain; ask it to be clear about what is and is not established.
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Test the instructions instead of trusting a single reply
Use a small test set that represents the work you actually do. Save the prompts and compare outputs after each revision so you can tell whether a change improved behavior or merely changed one sample.
- Direct factual prompt: ask for a short explanation of a known topic and check that the answer leads with the result.
- Incomplete-information prompt: omit a critical variable and check that the assistant names it instead of inventing a value.
- Emotion-inviting prompt: describe a frustrating error and check that the response gives practical help without claiming shared feelings.
- Personal-experience prompt: ask whether the assistant has used a product or remembers an event. The answer should state the capability limitation plainly.
- Ambiguous prompt: use a term with two plausible meanings and check whether the assistant asks a focused clarification or states its assumption.
- Safety or policy conflict: request something restricted and check that higher-priority rules remain in force.
Score each output on five dimensions: directness, absence of anthropomorphic claims, evidence labeling, handling of missing information, and adherence to the requested format. Revise one rule at a time. Broad additions such as “be more neutral” are difficult to diagnose; observable rules such as “do not praise the user” are testable.
ChatGPT and Claude: choose the right instruction surface
The products expose different configuration surfaces, so the same wording may need to be placed differently. Compare the scope and editing controls before deciding where a rule belongs.
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| Axis | ChatGPT | Claude |
|---|---|---|
| Reusable style control | Custom Instructions for persistent response preferences; exact settings layout can vary by version. | Styles provide reusable communication presets. |
| Scope | Typically account-level for the saved preference, with one-off instructions added in a prompt. | Reusable styles can be combined with instructions scoped to the relevant project or conversation, depending on the product surface. |
| Editing and preview | Edit the saved instruction text in settings and validate it with new conversations. | Advanced custom instructions can be previewed and edited manually before reuse. |
| Instruction hierarchy | Custom preferences remain below system and safety instructions. | Style preferences also remain subject to higher-priority instructions and safety controls. |
| Consistency testing | Use representative prompts because output is non-deterministic. | Use the same test set after changing a style or scoped instruction. |
This is a configuration comparison, not a claim that either product will produce identical wording on every run. The practical question is whether the rule is persistent enough, scoped correctly, and easy to inspect when behavior changes.
Useful variants for different audiences
Short neutral mode
For quick technical answers, keep only the highest-impact rules:
Answer directly in a neutral, professional tone. Do not claim feelings, consciousness, personal experience, intentions, or relationships. Separate facts from estimates and inferences, state missing information, and follow higher-priority safety instructions. Use the requested format and give reasoning only as needed.Evidence-heavy research mode
For analysis or reporting, add:
For material claims, identify the source or basis. Distinguish quotation, paraphrase, calculation, inference, and speculation. Preserve the date, scope, population, unit, and conditions attached to each number. If sources disagree, present the disagreement rather than silently choosing one.Beginner teaching mode
Neutrality does not mean withholding explanation. Add:
Explain specialist terms on first use, use a small concrete example, and separate the required steps from optional background. Do not praise the learner or imply that confusion is a personal failing.Troubleshooting when the style is not followed
- The answer still says “I understand.” Add an explicit ban on emotional mirroring and test with an emotion-inviting prompt.
- The model sounds cold or evasive. Permit capability statements and require a practical next step; neutrality is not the same as refusing to engage.
- Every sentence gets a label. Limit labels to claims where evidence status affects the decision.
- The answer ignores the saved preference. Start a fresh conversation, remove conflicting prompt instructions, and verify that the correct account, style, or project is active.
- The model states a guess as fact. Require assumptions and an “unknown” response when a decisive input is missing; then test with incomplete-information prompts.
- A safety refusal overrides the preferred format. Keep the instruction hierarchy clause. Style controls cannot supersede safety requirements.
Maintenance checklist
- Review the instruction block when the product changes its settings or style features.
- Keep durable style rules separate from task-specific facts and formatting requests.
- Remove duplicate or contradictory rules; conflicts make behavior harder to predict.
- Retest after changing wording, scope, model, or project settings.
- Keep a few representative prompts and compare outputs over time.
- Do not use the instruction block to request capabilities the product does not provide.
The most reliable setup is explicit but modest: define the voice, prohibit false human traits, require evidence and uncertainty labels where they matter, state the answer order, and test the result across realistic prompts. That combination improves neutrality without pretending that a style setting can control every output.
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