In enterprise AI, the glamorous part is still the model. Bigger context windows, better agents, faster inference, shinier demos—the usual fireworks display. But for many real deployments, especially in healthcare, legal review, insurance, industrial inspection, and compliance, the real bottleneck is less theatrical: labeled data. Not just data. Labeled data. Not just labeled data. Correct labeled data. ...
Opening — Why this matters now The easiest AI demo in the world is a model producing something plausible. A product description. A support reply. A defect image. A peer-review report. A compliance explanation. A benchmark answer. The output looks competent enough to be shown in a slide deck, which is often where corporate AI strategy goes to enjoy a short but well-lit life. ...
Training usually ends with a green tick. Employees watch a video, answer several questions whose correct responses are not exactly mysterious, and confirm that they understand the policy. The organization records completion. Everyone returns to work with roughly the same judgment they had before, plus one more certificate in the learning-management system. ...
RAG is a very polite librarian. It fetches documents, quotes passages, and helps an agent look less ignorant in public. Then the agent closes the book, answers the user, and leaves no trace except a chat log, a cache entry, or perhaps another small pile of private “reflections” that no one else will ever see. ...
A green block, a yellow block, and a very small number Green on yellow. That is the task. A tabletop robot sees a green block, a yellow block, and a few other objects. It has low-level manipulation skills. It receives a high-level instruction: put the green block on top of the yellow block. This sounds like exactly the kind of small benchmark task that modern AI agents should now handle with theatrical confidence. ...
TL;DR for operators Labels are the awkward invoice behind modern alignment. RLHF looks elegant in diagrams: generate outputs, ask humans which one is better, train a reward model, optimise the policy, repeat until everyone pretends the reward model is civilisation. In practice, most preference comparisons are not equally useful. Some are obvious. Some are redundant. Some teach the model almost nothing except that annotator budgets have a sense of humour. ...