Intermediatethat

Intermediatethat is a neologism used to refer to the practice of explicitly identifying and communicating the intermediate steps, representations, or propositions that lie between an initial premise and a final conclusion. The term does not reflect a single, formal discipline; instead, it appears in discussions across logic, cognitive science, and artificial intelligence as a label for stepwise reasoning that emphasizes intermediate elements rather than only the endpoint.

Etymologically, intermediatethat blends the word intermediate with the demonstrative that, signaling a focus on intermediate components. In online discourse and informal writing, it is often invoked to contrast end results with the more transparent process that leads to them, highlighting the value of showing work or reasoning paths.

In AI and related fields, proponents argue that intermediatethat-style approaches, prompts, or architectures help trace the reasoning path, enabling debugging, auditing, and greater interpretability. Critics warn that requiring explicit intermediate steps can bias outcomes, reveal sensitive internal states, or incur overhead without guaranteeing improved performance on all tasks.

Practically, applying intermediatethat involves generating or presenting a sequence of intermediate claims, transformations, or data representations that culminate in a final answer. In mathematics and problem solving, this mirrors the conventional practice of showing work; in natural language generation, it corresponds to providing chain-of-thought–style explanations.

See also chain-of-thought, explainable AI, and intermediate representation. As a term, intermediatethat remains informal and is not part of formal disciplinary glossaries, persisting primarily in speculative or hobbyist discussions about reasoning transparency.