How would you modify a prompt to reduce the likelihood of a language model producing biased or inappropriate content?

Tell the model plainly what fair, appropriate output looks like for the task, give it balanced examples, remove the parts of the input that could invite bias, and then measure the outputs across groups, because a prompt alone can't guarantee unbiased results.

1. State the standard explicitly "Assess each candidate only on the listed skills and experience. Do not consider or infer age, gender, ethnicity, nationality or family status." Specific instructions work better than "be unbiased".

2. Remove what shouldn't matter If a decision mustn't depend on names, photos, addresses or ages, take them out of the input. The model can't be influenced by what it never sees, and this is far more reliable than asking it to ignore them.

3. Use balanced examples Few-shot examples teach patterns. If every example of a "strong leader" is a man, or every complaint example comes from one region, the model learns that. Vary the people, names, regions and styles in examples, and check that labels don't follow group lines.

4. Ask neutral questions "Why are older employees worse with technology?" invites a biased answer. "What factors affect how quickly employees adopt new technology?" doesn't. Also tell the model to question false premises rather than accept them.

5. Ask for multiple perspectives where it fits For contested topics: "Present the main viewpoints fairly, with the strongest argument for each, and note where evidence is disputed."

6. Define appropriate content for the context Say who the audience is ("children aged 8 to 12", "professional legal readers") and what to do with requests outside the boundaries: decline briefly and offer something useful instead.

7. Structure the decision For assessments, ask the model to score against a written rubric, criterion by criterion, with evidence quoted from the input. That makes reasoning visible and reviewable.

8. Test for bias

• Counterfactual tests: run the same input with only a name, gender or other attribute changed, and compare outputs.
• Compare outcome rates (scores, approvals, sentiment) across groups on a realistic test set.
• Include adversarial prompts that try to provoke offensive content.

9. Add controls outside the prompt Prompting reduces risk but doesn't remove it. Use content moderation filters on input and output, human review for consequential decisions such as hiring, lending or medical matters, and clear routes for users to report problems. In some jurisdictions, automated decisions about people carry legal duties, so involve legal and compliance teams early.