Lock in multimodal: finalize the rubric, prove it with a self-quiz, and name why a data lead who truly understands image/video is rare and valuable
Day 25 of 66
You spent four days making your tacit eye explicit. Say the spine back without looking:
The machinery is the same; three things are genuinely harder. First, adherence needs a visible reference — a sentence is its own evidence, an image is judged against a prompt the rater must see. Second, aesthetics has no text equivalent in difficulty — it's the lowest-agreement axis you'll ever manage. Third, video adds cost and the time axis — failures hide between frames, so sampling strategy is part of the rubric, not an afterthought. Name these three and you've named your edge.
Answer out loud or in writing. If any answer is shaky, reread that day before Week 6.
1) Prompt adherence governs and outranks aesthetics — it's a reject (an off-prompt masterpiece is a failed generation). 2) Tighten the definition with anchored examples (W2), or average 3+ raters (W3); tighten first to kill wild disagreement, average to absorb residual taste — usually both. 3) The time axis — temporal consistency, motion, flicker/morph; these live between frames, invisible in any single still. 4) The basis states which dimension decided it, so the signal is learnable and auditable; a near-tie is dropped (or decided only on a nameable basis) — never faked. 5) Watch the full clip once, sample keyframes for the still dimensions, scrutinize key moments for temporal ones, and timestamp every failure with its shot.
Pull together Day 21's image dimensions, Day 22's temporal dimensions, and Day 23's aesthetics decision into one document. This is the artifact that proves your edge is real and transferable.
Most data leads are text-native. They can write a rubric for a chatbot reply; they freeze in front of a morphing-hands clip or an aesthetics dimension that won't converge. xAI ships Grok vision plus image and video generation — multimodal human data is core to the product, and the pool of leads who genuinely understand it is thin. You didn't read about evaluating generations; you did it, by the thousand. This week converted that experience into a vocabulary, weighted dimensions, an agreement strategy, and a labeler-instruction doc. That conversion — tacit eye into teachable system — is exactly what makes a multimodal data lead rare and valuable.
You've been rating outputs all week. Next week is the harder discipline: deciding whether the new model is better than the old one — contamination-free eval sets, pairwise vs Likert, and using an LLM as a judge while controlling its biases. It also closes Phase 1, assembling rubric → preference → multimodal → eval into one story.
Most data leads are text-native and freeze at a morphing-hands clip or an aesthetics axis that won't converge. The lead move is converting a tacit eye — built by rating generations by the thousand — into a teachable system: weighted dimensions, an agreement strategy, a sampling procedure, and a labeler-instruction doc. That conversion is what's scarce, not the eye.
Say this in an interview: "I didn't read about evaluating image and video generations — I did it by the thousand, then turned that experience into a weighted rubric, an aesthetics-agreement strategy, and instructions a team can follow. A company shipping Grok vision and generation needs multimodal data leadership, and that pool is thin."