Service explainers
What is human data for llm projects and how does it work?
Updated 2026-08-01 · 4 min read
Short answer
Human-generated text and speech for LLM training and evaluation in Indian languages: prompts, preference rankings, instruction-response pairs, red-teaming and cultural-fit review. In practice the work runs as task specification and rubric design, then contributor screening against the rubric, then calibration round with feedback, and you receive task data in your schema, rubric and calibration results. 2-6 weeks depending on task complexity and contributor screening depth.
Key takeaways
- Every item is traceable to a screened contributor, which matters when a model vendor audits your data provenance.
- Typical buyers: LLM fine-tuning, RLHF-style preference data, Multilingual evaluation.
- Recruitment approach: Contributor pools are built per domain; the same pool is not reused across conflicting tasks.
What the service covers
Human-generated text and speech for LLM training and evaluation in Indian languages: prompts, preference rankings, instruction-response pairs, red-teaming and cultural-fit review.
- Task data in your schema
- Rubric and calibration results
- Contributor metadata (anonymised)
- Agreement statistics
Technical specification
These are defaults, not limits. Where your pipeline requires different values, they replace ours in the statement of work rather than being converted after delivery.
| Parameter | Standard |
|---|---|
| Task types | Prompt writing, response ranking, instruction-response pairs, adversarial testing |
| Languages | Any language in the network, including code-mixed Hinglish |
| Contributors | Screened by domain, education band and language proficiency |
| Agreement | Overlapping assignments with adjudication |
| Provenance | Per-item contributor and time records |
How the work runs
- Task specification and rubric design
- Contributor screening against the rubric
- Calibration round with feedback
- Production with overlap and gold items
- Adjudication and delivery
Quality control and acceptance
Every item is traceable to a screened contributor, which matters when a model vendor audits your data provenance.
Failures are remedied by re-collection rather than by editing delivered files, because repaired audio carries artefacts that survive into the trained model.
Who this is for
Recruitment for this service works as follows. Contributor pools are built per domain; the same pool is not reused across conflicting tasks.
- LLM fine-tuning
- RLHF-style preference data
- Multilingual evaluation
- Red-teaming
Timelines
2-6 weeks depending on task complexity and contributor screening depth.
Multi-language programmes run in parallel rather than in sequence, so a five-language scope does not take five times as long.
Frequently asked questions
What is included in human data for llm projects?
Task data in your schema, Rubric and calibration results, Contributor metadata (anonymised), delivered against a written specification with acceptance criteria attached.
How long does human data for llm projects take?
2-6 weeks depending on task complexity and contributor screening depth.
How is quality measured?
Every item is traceable to a screened contributor, which matters when a model vendor audits your data provenance.
Which languages are supported?
Fifteen Indian languages plus Indian English, including Hindi, Marathi, Tamil, Telugu, Kannada.
Related reading
Turn this into a dataset specification
Tell us the languages, speaker count and minutes. You get a written scope, a protocol and a fixed price within one working day.