Turnaround
How long human data for llm projects takes
Honest delivery schedules for human data for llm projects, the stages that actually consume the calendar, and the three levers that safely compress a timeline.
- Standard
- 2-6 weeks depending on task complexity and contributor screening depth.
- Pilot
- 10-20 units in 1-2 weeks
- First batch
- Typically week 3
- Cadence
- Weekly batches after the first
Where the time actually goes
Recording is rarely the bottleneck. Specification agreement, speaker recruitment against quotas and native-reviewer QA are what set the calendar, and all three run in parallel once the spec is signed.
- Task specification and rubric design
- Contributor screening against the rubric
- Calibration round with feedback
- Production with overlap and gold items
- Adjudication and delivery
Indicative schedules
| Volume | Spec + setup | Collection | QA + delivery | Total |
|---|---|---|---|---|
| Pilot (10-20 units) | 3-5 days | 5-7 days | 3-4 days | 1-2 weeks |
| 100 units | 1 week | 2-4 weeks | 1 week | 4-6 weeks |
| 500 units | 1-2 weeks | 4-7 weeks | 1-2 weeks | 6-10 weeks |
| 1,000 units | 2 weeks | 7-11 weeks | 2 weeks | 10-16 weeks |
Batches are delivered as they pass QA, so you start training long before the final unit lands.
What safely compresses a timeline
- Parallel cities: the same cohort recruited across several studios at once rather than sequentially
- A signed specification on day one — most slipped schedules start as an unsettled spec
- Accepting rolling batches instead of a single final delivery
- Widening one non-critical quota (usually education band or district) while holding the ones that matter
What cannot be rushed
Recruitment of narrow cohorts, native review depth and consent administration have floors. Compressing them is how vendors end up delivering data that fails acceptance, which costs more calendar time than it saved.
Every item is traceable to a screened contributor, which matters when a model vendor audits your data provenance.
What lands with each batch
- Task data in your schema
- Rubric and calibration results
- Contributor metadata (anonymised)
- Agreement statistics
Frequently asked
What is a realistic turnaround for llm human data?
2-6 weeks depending on task complexity and contributor screening depth. for a standard production run, with a pilot possible in one to two weeks and first batches usually arriving in week three.
Can you hit a hard deadline?
Often, by running more cities in parallel and delivering rolling batches. We will say no rather than accept a date that forces a quality compromise.
Do we wait for everything before we can train?
No. Batches are delivered as they pass QA so training can start early and the spec can be adjusted while collection is still running.
What causes delays in practice?
Late specification sign-off, mid-project quota changes and narrow cohorts in low-density districts. All three are visible in the plan before we start.
Related pages
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