What type of language does meta-model questioning aim to clarify?

Meta-model questioning is a technique used in NLP to clarify vague language and challenge assumptions. The goal of this approach is to identify and unpack the statements made by individuals to enhance understanding and remove ambiguities.

When individuals use vague language, they often generalize or omit important details, leading to misunderstandings. Meta-model questions aim to elicit more specific information, which allows the practitioner to clarify meanings and assumptions that may underlie the statements being made. This not only helps in achieving better communication but also in identifying limiting beliefs that can hinder someone's progress or understanding.

For instance, if someone says, "I always fail," a meta-model question would probe for specifics by asking what precisely they mean by "always" or to provide examples of their failures. This process brings to light the nuances and beliefs behind their language, allowing for a deeper resolution of the issues at hand. Therefore, focusing on vague language and challenging underlying assumptions is central to the effectiveness of the meta-model questioning technique.