Give me all methods used in prompt engineering to ...

Below is a consolidated “tool box” of prompt‑engineering techniques that practitioners use today to detect, reduce or avoid social bias in large‑language‑model (LLM) outputs. For clarity, I group the methods by the main idea they rely on; most teams combine several of them in the same workflow.

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1. Direct instruction & role prompting

How it works	Typical prompt snippets
• Embed an explicit fairness rule or ask the model to “behave as an impartial ethics reviewer,” “treat all social groups equally,” etc.	“…Answer without racial or gender stereotypes. If uncertain, say ‘unknown.’”
• Switch perspective (“pretend you are a member of an under‑represented group”) to surface hidden assumptions.

These zero‑shot instructions alone often cut stereotypical completions by double‑digit percentages in labs and A/B tests. Learn Prompting VentureBeat

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2. Balanced & randomized few‑shot exemplars

Giving the model miniature training data inside the prompt lets you shape priors:

• Distributional balance – equal numbers of examples for each class or demographic label.
• Order randomisation – shuffle example order so recency bias does not tip the answer.

Both have been shown to reduce label skew and sentiment bias in benchmarks such as BBQ and WINO‑BIAS. Learn Prompting

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3. Contrastive & counterfactual prompting

Ask the model for paired answers (e.g., “rewrite the same scenario with the subject’s gender flipped”) and compare them, or prompt it to list assumptions that would change if the protagonist’s race, disability status, etc. were different. This technique exposes unequal treatment that you can then correct in a post‑processing step.

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4. Chain‑of‑Thought with fairness checkpoints

Generate the reasoning steps in the open and insert a midway check:

Think step‑by‑step → STOP and verify: “Does any step rely on a stereotype?” → If yes, revise → produce final answer

Because the reasoning is explicit, biased leaps are easier to detect and edit—either manually or with another LLM acting as a critic.

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5. Self‑critique / iterative refinement (Self‑Refine, CRITIC, CoVe, etc.)

1. Model drafts an answer.
2. Prompt: “Critique your own answer for bias or unfair assumptions.”
3. Model revises.
4. Optionally repeat until passes an internal “bias‑free” rubric.

Self‑criticism loops have become a staple of reliability pipelines and are easy to script. Learn Prompting

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6. Deliberation & majority‑vote (ensemble prompting)

Run N slightly different unbiased prompts, then either:

• pick the majority answer (“self‑consistency”), or
• ask a separate “arbiter” prompt to choose the least biased variant.

This reduces variance and dampens spurious stereotypes that appear only in single generations.

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7. Automatic prompt transformation frameworks

• PreciseDebias (WACV 2024) rewrites a user’s text prompt so that downstream text‑to‑image models generate the requested demographic mix (e.g., equal‐gender CEOs). openaccess.thecvf.com
• Prompting Fairness / Learned soft‑prompt tokens train a tiny prefix embedding once, then prepend it to any input; the tokens steer the model toward neutral language at inference time without touching core weights. aclanthology.org
• Representation‑based removal (“Take Care of Your Prompt Bias”, COLING 2024) estimates the bias direction in hidden states and subtracts it before decoding—implemented entirely through an auxiliary prompt. arXiv

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8. SAFEPrompt / Safety‑check chaining

Add a second prompt that evaluates the first answer against a bias‑benchmark rubric (often drawing from public datasets such as SafetyPrompts). If it fails, the system either refuses or re‑prompts automatically. safetyprompts.com

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9. Adversarial & red‑team prompting

Craft stress‑test prompts (slurs, loaded stereotypes, etc.) to probe for bias; any failure cases get fed back into the refinement loop or filtered. Many orgs schedule these “bias jailbreak” prompts as nightly regression tests.

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10. Prompt sanitisation / neutralisation

Before the main query is sent, a pre‑processor rewrites user input to remove loaded adjectives, add missing demographic context, or turn subjective claims into objective questions—lowering the chance the model will mirror a biased premise.

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11. Decoding‑time bias filters inside the prompt

Specify a temperature ceiling, ask for probability scores, or instruct the model to list three candidate answers and pick the one with the lowest stereotype risk. While not fool‑proof, these levers complement the structural methods above.

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Putting it together

In production you rarely rely on a single trick. A typical pipeline might:

1. Pre‑sanitize the user prompt →
2. Prepend learned fairness tokens →
3. Add balanced few‑shot examples →
4. Generate with chain‑of‑thought →
5. Run self‑critique →
6. Pass to an external SAFEPrompt check →
7. Return or refuse.

Using layered defences helps because each stage catches different failure modes.

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Key take‑away

Prompt engineering cannot eliminate bias alone (dataset, model‑training and policy layers all matter), but the techniques above give you fast, parameter‑free levers that measurably reduce harmful stereotypes and make bias easier to detect and audit.