Data

Data labeling that scales without poisoning your dataset

High-volume labeling is useless if definitions drift or edge cases are inconsistent. Invest in the process first.

Write the guideline before the queue

Concrete examples of accept/reject reduce reviewer disagreement. Revisit the doc when error patterns shift.

Overweight rare but critical segments—safety, long tail entities, low-resource languages—so metrics reflect real risk.

Spot-check batches and measure inter-annotator agreement. Drift shows up as silent quality decay.

Same cards as the blogs page—related topic first, then newest.

A practical order of operations: abstract, figures, method, then experiments—so you know what to skim and what to study.

Latency, batching, KV-cache memory, and evaluation—not just swapping in a bigger model.

If retrieval misses the right chunk, no prompt tweak will save the answer. Split your eval into recall, grounding, and fluency.

Go deeper on optimization, evaluation, and data—without replaying every undergraduate lecture.