quality

Machine translation is seq2seq generation delivered across hundreds of language pairs under strict latency and quality budgets. What matters is sidestepping the quadratic pair blowup, saving low-resource languages, and serving it cheaply. Here is the design.

Labeled data is the fuel for ML, and a labeling platform succeeds or fails on quality control. The signal is the workflow plus the quality math: consensus, gold honeypots, inter-annotator agreement, and active learning to spend the budget where it counts.

RLHF and evals can only match the preference data behind them, and that data comes from humans whose quality swings wildly. The platform that yields trustworthy labels is a serious system in its own right. Here is its design.

Every AI system spends quality to buy speed, whether or not the team admits it. What scores is knowing the exchange rate for each knob, taking the free wins first, and having already decided what you will sell when the system saturates.