Few-shot prompting is a technique for guiding AI model behavior by including a small number of examples (typically 2–5) in the prompt itself — showing the model the pattern you want it to follow before asking it to perform the task.
Contrast with zero-shot: Zero-shot asks the model to perform a task with no examples. Few-shot provides examples to establish the pattern.
Few-shot prompting example
Classify each query by intent:
Query: "What is AEO?" → Intent: Informational
Query: "Ahrefs pricing" → Intent: Navigational
Query: "Best SEO tool for agencies" → Intent: Commercial investigation
Now classify: "Sign up for LLM Metrix free trial" → Intent:
The model learns the classification task from the examples and applies it to the new query.
Relevance to AEO research
Few-shot prompting is useful for AI visibility research and competitive intelligence:
Persona simulation: “You are a VP of Marketing at a 200-person SaaS company. Here are three questions you asked a consultant last month: [examples]. Now ask three questions about AI visibility tools.” This surfaces the query patterns your actual buyers use.
Brand framing research: By providing examples of how a competitor’s brand is described, you can probe how a model represents your brand under similar framing conditions — surfacing implicit biases or knowledge gaps in brand representation.
Monitoring query design: Few-shot prompting can help calibrate the exact phrasing of your monitoring query set to more closely approximate natural buyer behavior.
For most brand monitoring purposes, standard zero-shot queries are sufficient — but few-shot prompting is a useful advanced technique when designing research prompts or investigating specific framing effects.