Most of us learned to prompt AI by guiding its thinking. "Think step by step." "Here's an example of how to solve this." "First check A, then compare B, finally conclude with C." These techniques made sense — because we were working with models that needed a path. Without structure, they'd rush to a conclusion. But reasoning models shift this premise. Thinking Comes Before the Answer General conversational models excel at producing natural answers quickly. For tasks where the direction is clear — brief summaries, simple explanations — this is sufficient. Reasoning models work differently. Rather than pushing problems straight toward conclusions, they're designed to compare conditions, trace possible paths, and hold problems longer before forming answers. Models like Claude's extended thinking or OpenAI's o-series represent this direction — built to spend more computation on internal reasoning. A reasoning model isn't one that writes longer answers. It's one built to grapple with harder problems for longer.…