Why it matters
Different archetypes surface different brands. A problem-first prompt (“my skin gets oily by afternoon”) may mention different brands than a comparison prompt (“boat vs noise earbuds”) or a budget-anchored prompt (“affordable noise cancelling earphones”). A brand that dominates recommendation prompts but is invisible on comparison prompts has a specific content gap to fill. Archetype-level analysis reveals these gaps.How Cited uses it
The pipeline generates prompts across all six archetypes with target distribution weights (problem_first 25%, context_specific 20%, budget_anchored 15%, comparison 15%, recommendation_seeking 15%, feature_curious 10%). This ensures a brand’s prompt library covers the full range of customer question styles. Dashboard analytics support archetype-level filtering for precise visibility diagnostics.Related concepts
- How we generate prompts — the pipeline that classifies prompt archetypes
- Intent type — the broader intent classification that archetypes sit within
- Prompt engineering — the discipline of crafting effective prompts