Prompt engineering (for GEO monitoring)
Building the right questions to get reliable tracking of a brand’s visibility.
Definition
In the context of GEO, prompt engineering refers to the methodical construction of a set of prompts representative of the real questions asked by a market, used to track a brand’s presence in AI answers in a comparable way.
Key takeaways
- A good prompt set covers brand queries, category queries, comparisons and recommendation requests.
- Prompts must stay stable over time to allow reliable comparison.
- A poorly phrased prompt can distort the reading by artificially steering the answer.
- Prompt engineering for monitoring differs from the prompt engineering used to drive a model in production.
Explanation
Unlike prompt engineering applied to the direct use of a model (getting a better answer from an assistant), prompt engineering applied to GEO monitoring aims to faithfully reproduce the questions a real prospect or a real customer would naturally ask.
This means covering several prompt families: direct questions about the brand, category or discovery questions, comparison or alternative requests, and explicit recommendation questions.
The rigor of this construction determines the reliability of the entire tracking process: prompts that keep changing or are poorly calibrated make any comparison over time or across engines impossible.
Go further
AIglebotBetaAIglebot helps companies track their visibility, reputation and recommendation in ChatGPT, Gemini, Perplexity and Claude.