Artificial intelligence (AI) simplifies complex language by inferring the meaning of a sentence based on patterns learned from vast amounts of human-generated text. This is how Google has refined its search engine for years. By analysing search queries like “NLP Northern Ireland certification”, it determines user intent. In this case, an intent to buy a course and serves relevant advertisements.
This predictive capability is also central to conversational AI like ChatGPT. It analyses a prompt and, based on its training data from countless similar conversations, predicts the most probable and satisfactory response. It appears intelligent, but it is essentially building on patterns from billions of previous human interactions to generate a likely response.
In contrast, Neuro-Linguistic Programming (NLP) practitioners operate differently. Instead of predicting common patterns, their goal is to break them and encourage new ways of thinking. They focus on the structure and process of language, not its content or most probable meaning.
An NLP practitioner identifies patterns across various levels:
Beliefs and emotions.
The structure of words (presuppositions).
Different levels of abstraction (chunking).
Deletions, distortions, and generalisations in speech.
They ask specific questions to uncover missing information and challenge assumptions. A key part of this involves identifying “prime concerns” and “analogically marked sentences” such as subtle shifts in vocal tone, emphasis, or emotional charge that reveal deeper meaning.
Current AI systems struggle with this because they typically convert speech to text, stripping away the crucial emotional and analogical data that humans perceive instinctively.
An AI cannot detect the vocal emphasis or emotional charge in a sentence, which are vital for a practitioner’s interpretation.
While future AI may develop this capability, it currently lacks the nuanced understanding required for true NLP analysis