Search looks cheap until you try to train it. A business can usually collect plenty of questions. Employees ask support bots why a policy changed. Analysts ask internal search systems for comparable transactions. Legal teams ask where a contract clause first appears. Researchers ask agents to chase a multi-step trail across documents, web pages, and databases. ...
Search is supposed to make language models safer. The model does not know something, so it searches. It finds evidence, reasons over that evidence, and gives a better answer. Very civilized. Very responsible. Then the first search query goes slightly wrong. The model retrieves a relevant-looking but misleading paragraph. It builds the next reasoning step around the wrong entity. The next query becomes narrower, but in the wrong direction. The final answer may still sound fluent, because fluency is the one department where language models rarely file sick leave. The actual reasoning chain, however, has already drifted. ...