LLM probing is the practice of systematically querying a specific language model with a defined set of prompts to assess how the model represents a brand, to…
LLM probing is the practice of systematically querying a specific language model with a defined set of prompts to assess how the model represents a brand, topic, or category — extracting the model’s current “knowledge state” about a subject for diagnostic and optimization purposes.
LLM probing is a core research method for AI citation audits. By running a systematic battery of prompts — direct brand queries, category queries, competitor comparisons, and topic association queries — practitioners can map what a specific model knows and does not know about a brand, identify inaccuracies, and benchmark current AI representation before and after optimization interventions. Each model requires separate probing because their representations differ.