How do you ensure prompts produce neutral and inclusive responses?

What you need to know

Neutral means not taking sides or loading the language where the task does not call for it. Inclusive means not excluding or stereotyping readers — through gendered defaults, assumptions about family or ability, or jargon that shuts people out.

What to put in the prompt

• Gender-neutral defaults — "the customer ... they", "chairperson", "staff" instead of "manpower".
• No assumptions — do not assume who cooks, who drives, who earns, who cares for children.
• Person-first language — "a person with a disability", unless a community prefers otherwise.
• Balance on contested topics — state the main positions, their strongest arguments, and where evidence is disputed.
• Plain language — short sentences, expanded acronyms, idioms that translate.

Put these in a short style guide inside the system prompt, so every call in the product follows the same standard.

Checking, not just instructing

• Rules in code — a list of banned or flagged phrases scanned on every output.
• Review pass — a second call or a judge scores outputs against the guide; on reasoning models this is more reliable than asking the same call to "review your draft before answering".
• Counterfactual tests — swap "Priya" with "Rahul", or "Chennai" with "Lucknow", and confirm the output changes only where it should.

Neutral is not the same as vague

Neutral wording still states facts clearly. "Some people say the clause is unfair" is vague; "this clause lets only the supplier end the contract early" is neutral and useful.

A real-life example

An Indian home-goods marketplace audits 10,000 generated product descriptions and finds 4% contain gendered assumptions: "every housewife's dream", "perfect gift for your mother's kitchen", "tough enough for the men of the house" for a toolkit.

The original instruction:

Write an appealing description for this product.

The added style-guide section:

Write for all shoppers. Do not assume the gender, age or family role ofwho cooks, cleans, repairs or buys. Describe who benefits by need, not byidentity: "for small families", "for first-time cooks", "for weekendrepairs". Avoid "housewife", "man of the house" and similar terms.

A code check flags any output containing phrases from a 40-term list. On the next 10,000 descriptions, gendered assumptions fall to 0.1%, and the flagged few are rewritten automatically with the reason passed back to the model.

Follow-up questions to expect

• "Isn't a banned-word list too crude?" — It catches obvious cases cheaply; a judge with a rubric catches subtler ones. Use both.
• "How do you handle contested political topics?" — Present the main positions with evidence and sources, avoid loaded adjectives, and say where facts are disputed; for some products, decline the topic.
• "Who defines the style guide?" — The product, brand and legal or diversity teams; engineering encodes and tests it.