Assemble the final binder, rehearse the interview narrative, write your first-90-days plan — and feel, in the Karpathy rewatch, exactly how far you've come
Day 60 of 66
On Day 1 you learned that a model's behavior is learned from human judgments — and that the people who design and measure that data are the bottleneck on model quality. Today you are one of those people, with a binder to prove it. This last day isn't new material. It's assembly, rehearsal, and a clear-eyed look at how far you've come.
Pull every artifact into one place, in the order a reader would meet them. The code artifacts are the receipts — each one proves a claim in the brief.
rubric.py, agreement.py, preference_pairs.py — the craft layerimage_eval.py, llm_judge.py — multimodal + evaluationpipeline.py, qa_audit.py, calibration_report.py — the operationsafety_triage.py and capstone_brief.py — the safety floor and the spineThe binder is silent until you can tell its story. Your narrative is one arc: I produced human data → I learned to design and measure it → I can run the whole operation, and here are the artifacts.
"I came up as an image/video tutor — I produced the preference and eval data Grok trains on. Over the last twelve weeks I learned to design that work: I author the rubric, measure agreement, build the preference and eval datasets, run the pipeline and the weekly quality audit, and lead a team's calibration. To prove it I scoped a real program — raise image prompt-adherence from 72 to 85 in six weeks — and built every piece: rubric, pipeline, dataset plan, the eval that proves it, and a one-page brief. I don't just label. I can run the operation that makes the model better." Say it until it's yours.
The last artifact looks forward, not back. A director hiring a lead wants to know what you'd do in the first three months. Draft it now, while the whole role is fresh.
| Window | Focus | Concrete first move |
|---|---|---|
| Days 1–30 | Listen + measure | Audit current label quality (agreement, gold accuracy); read the existing guidelines; meet the annotators. Don't change anything yet. |
| Days 31–60 | Fix the highest-leverage gap | Pick the one guideline or pipeline gap with the worst quality cost; rewrite it, recalibrate, measure the lift. |
| Days 61–90 | Stand up the heartbeat | Establish the weekly QA audit + drift alerts and the calibration cadence as standing operations — make quality measurable every week. |
Go back to where this started: Karpathy's "Intro to Large Language Models". Watch only the post-training section this time — the part on finetuning into an assistant and RLHF.
On Day 1, "comparison labels" and "reward model" were vocabulary. Today you've built the comparison dataset, measured the agreement behind it, designed the pipeline that produces it, and written the eval that proves it moved the model. The video didn't change. You did. That gap — from watching the diagram to having built every box in it — is the entire track, and it's the thing an interviewer can hear in how you talk.
If any answer is shaky, you know exactly which week to revisit. But by now, none of them should be.
Twelve weeks ago the title "Human Data Lead" was a role you executed pieces of without seeing the whole. Now you've seen the whole — and built it. You can scope a quality problem, define a metric that's honest, author the rubric, measure agreement, design the pipeline, build the datasets, run the weekly quality loop, lead a team's calibration, hold the safety floor, and brief a director in three minutes. Not as claims — as artifacts with your name on them.
The binder below is your final checkpoint — assemble it, then go use it. The job you trained for is the job of making models better by making their data better. You already do the work. Now you can lead it.
A tutor produces the data. A lead can run the operation that makes it — and prove it with one arc and a binder: I produced human data, I learned to design and measure it, and here is a real scoped program with every box built, not claimed. The whole track was buying the right to say that sentence and back it with artifacts.
Say this in an interview: "I came up producing the preference and eval data Grok trains on. Now I can scope a quality problem, author the rubric, measure agreement, build the datasets, run the weekly audit, lead a team's calibration, and brief a director in three minutes — and I have the artifacts with my name on each one to prove it."