Model & data planning
How much training data do you need for llm evaluation?
Updated 2026-08-01 · 4 min read
Short answer
For llm evaluation, volume matters less than composition. Human evaluation of large language model output in Indian languages, including cultural and factual fit. The corpus profile that works is native-speaker rater panels per language, rubric-based scoring with calibration, overlapping assignments for agreement. Start with a pilot sized to move rubric scores with confidence intervals, inter-rater agreement measurably, confirm the gain on held-out data recorded under deployment conditions, then scale the configuration that worked rather than scaling everything.
Key takeaways
- Success is measured on rubric scores with confidence intervals, inter-rater agreement, failure-mode distribution.
- The most common failure is raters who are fluent but not native in the variety
- Data profile: native-speaker rater panels per language, rubric-based scoring with calibration, overlapping assignments for agreement.
What the model actually needs
Human evaluation of large language model output in Indian languages, including cultural and factual fit.
- Native-speaker rater panels per language
- Rubric-based scoring with calibration
- Overlapping assignments for agreement
Metrics that tell you when you have enough
Collect against a metric, not against a number of hours. When a pilot batch moves the metric and a second batch of the same profile moves it less, you are at the point where composition, not volume, is the constraint.
- Rubric scores with confidence intervals
- Inter-rater agreement
- Failure-mode distribution
Common mistakes
- Raters who are fluent but not native in the variety
- Rubrics written in English and applied to non-English output without localisation
A sensible collection sequence
| Phase | Volume | Purpose |
|---|---|---|
| Pilot | 10–20 hours | Validate format, acoustics and annotation against your pipeline |
| First production batch | 100–300 hours | Move the primary metric and expose composition gaps |
| Targeted top-up | 50–150 hours | Fill the specific dialects, conditions or edge cases the eval exposed |
| Evaluation set | 5–20 hours | Held-out, deployment-condition data never used for training |
Services that supply this data
This use case is normally served by human data collection for llm, ai voice evaluation, translation and localisation. Most programmes combine two of them, because raw collection without matched annotation rarely moves an applied metric on its own.
Hold back an honest evaluation set
Reserve deployment-condition data that never enters training. Teams that evaluate on data recorded in the same sessions as their training data consistently overestimate real-world performance, then discover the gap after launch.
Frequently asked questions
What data profile suits llm evaluation?
Native-speaker rater panels per language, Rubric-based scoring with calibration, Overlapping assignments for agreement
Which metrics should we track?
Rubric scores with confidence intervals, Inter-rater agreement, Failure-mode distribution
What goes wrong most often?
Raters who are fluent but not native in the variety
Can we start small?
Yes. A 10–20 hour pilot delivered in your ingest format is the standard first step, and it usually exposes format or annotation mismatches that would have been expensive at volume.
Related reading
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