Few-shot prompting

Few-shot prompting includes 2–10 example outputs inside the prompt itself so the large language model can pattern-match its output against the examples — the standard technique for brand voice work.

Showing a model examples is a much stronger signal than describing what you want. Few-shot prompting gives the model a template to mimic: voice, format, length, structure. For brand voice, the examples are typically 3–8 of the brand’s past posts, ideally on a mix of topics so the model generalises rather than copying one specific piece.

Few-shot is the technique under the hood of most voice-aware AI marketing tools. Implementation quality varies: some tools use a fixed set of generic examples (defeating the purpose), some use the user’s last few posts regardless of relevance, and some use retrieval to pick the most relevant examples per request (the best approach).

Few-shot prompting is the difference between AI that has read your brand and AI that has read about brands. The number and relevance of the examples in the prompt is one of the strongest determinants of output quality.