Counterintuitively, teams that outperform with AI feel worse because they treat it as a true conversation partner, which is hard work. Underperforming teams treat AI like a magic oracle, which feels easy and satisfying but yields generic results.
An LLM's first response is engineered to be the most statistically likely answer, resulting in generic ideas. To achieve breakthrough results, you must treat it like a collaborator and push it with context, questions, and iterative feedback to move past its default.
The standard browser text box encourages a transactional, 'Googling' mindset. Interacting with an AI via voice on a mobile app leverages existing conversational habits, leading to richer dialogue, better context-sharing, and more creative outputs.
A powerful technique for refining ideas is to generate a response from one AI (e.g., ChatGPT) and feed that output to a different AI (e.g., Claude), asking it for a critique. This creates a multi-perspective dialogue that improves ideas beyond what a single model can achieve.
Instead of a transactional task, beginners should prompt an LLM about a real emotional dilemma. This forces a conversational, question-asking interaction that builds the correct mental model for using AI as a thinking partner rather than a search engine.