# Few-shot exemplars versus banned-word lists for style control > Source-anchored research for Few-shot exemplars versus banned-word lists for style control. Run status: COMPLETED. Parent: [Prompt-side control of LLM lexical tics](https://llms-explorer.com/tree/ai-isms-prompt-side-control/) · 4 facets · 7 facts · page: https://llms-explorer.com/tree/few-shot-exemplars-versus-banned-word-lists-for-style-control/ ## Definitions - Per-token banning is a poor substitute for prompt-side lists: most slop phrases span several tokens, banning 'catatonic' also bans words starting with 'cat', and token banning becomes unusable near 2,000 patterns while the Antislop sampler handled 8,000+. (low confidence) — [source](https://huggingface.co/papers/2510.15061) ## Structure and components - Anthropic's current prompting guide calls examples one of the most reliable ways to steer output format, tone and structure, and recommends 3-5 examples that are relevant, diverse and wrapped in tags. (medium confidence) (verified as of 2026-09-23) — [source](https://platform.claude.com/docs/en/docs/build-with-claude/prompt-engineering/multishot-prompting) ## How it works - The Antislop paper states that instructing a model to avoid a set of banned vocabulary has limited efficacy and may cause a backfire effect (the 'pink elephant problem'). (low confidence) — [source](https://huggingface.co/papers/2510.15061) ## Facts and statements - Anthropic warns that examples must be diverse enough that the model does not pick up unintended patterns, so exemplars can themselves transmit repeated wording. (low confidence) — [source](https://platform.claude.com/docs/en/docs/build-with-claude/prompt-engineering/multishot-prompting) - OpenAI's prompting guide advises placing tone and role guidance in the system message and keeping task examples in the user message, in a concise block; it gives no banned-word-list guidance. (low confidence) (verified as of 2026-09-23) — [source](https://developers.openai.com/api/docs/guides/prompting) - Few-shot style imitation has limits: across 400+ authors and 40,000+ generations per model, LLMs approximate styles in structured formats like news and email but struggle with informal blog and forum writing, and the number of demonstrations does not fully fix this. (low confidence) — [source](https://arxiv.org/abs/2509.14543) - Advocacy for banned-word lists (e.g. avoid 'delve', 'tapestry', 'realm') rests on social proof that readers spot and dislike AI words, not on measured effectiveness. (low confidence) (verified as of 2025-11-09) — [source](https://ruben.substack.com/p/delve)