Users do not ask questions in benchmark format. They ask in fragments, emails, forms, meeting notes, support tickets, spreadsheet comments, and occasionally in the sort of sentence that makes a compliance officer stare silently at the ceiling. A business AI agent does not receive one clean canonical prompt. It receives the same task wearing many costumes. ...
Feature engineering has always had a faint smell of kitchen experimentation. Take the raw variables. Add ratios. Try logs. Multiply this by that. Remove the ones that look useless. Feed everything into XGBoost. Pretend the process was scientific because the final notebook has a clean cross-validation table. In many business analytics teams, this is not a caricature. It is Tuesday. ...
Audio. Video. Subtitles. The standard instinct is to send all of them into the model and hope the transformer performs its usual magic trick: turn a messy pile of signals into a useful answer. This instinct is understandable. It is also expensive, noisy, and occasionally a magnificent way to teach the model the wrong lesson. ...
Bad data is not one problem. It is at least three problems wearing the same cheap trench coat. There is bad data that appears once and disappears. There is bad data that keeps appearing, but becomes rarer as the corpus grows. And there is bad data that settles in at a stable rate, like a permanent tenant with poor hygiene and legal representation. Business discussions about AI training data often compress these into one vague category called “noise”. Convenient, yes. Informative, no. ...