Training Dataset Generation
Training data is a product with provenance, rejection reasons, and release history—not a pile of examples.
Design source taxonomy, synthetic generation, independent audit, sensitive-data removal, coverage, and human sampling.
01
Problem definition
When the same model family generates and judges data, amplified errors can still receive high scores.
Data without source and transformation history cannot support deletion, correction, or retraining requests.
- Real data cannot sufficiently cover rare behavior and failure conditions
- Generation and human audit can be operated as versioned releases
- Source rights and sensitive-data boundaries are unverified
- The objective is volume without a coverage target
Training Dataset Generation: system plate
- 01
Stage 1
Classify source types, permitted transformations, retention, and deletion before collection.
- 02
Stage 2
Separate generator and auditor, combining rules, models, and human samples.
- 03
Stage 3
Record PII, domain secrets, and schema errors as independent rejection reasons.
- 04
Stage 4
Release only data that passes the coverage matrix and lineage manifest.
- The workflow begins with Classify source types, permitted transformations, retention, and deletion before collection..
- It reaches an acceptance decision through Source and transformation lineage completeness.
03
Design method
Training data is a product with provenance, rejection reasons, and release history—not a pile of examples.
- 01
Stage 1
Classify source types, permitted transformations, retention, and deletion before collection.
- 02
Stage 2
Separate generator and auditor, combining rules, models, and human samples.
- 03
Stage 3
Record PII, domain secrets, and schema errors as independent rejection reasons.
- 04
Stage 4
Release only data that passes the coverage matrix and lineage manifest.
04
Application scenarios
Real data cannot sufficiently cover rare behavior and failure conditions
When the same model family generates and judges data, amplified errors can still receive high scores.
- APPROACH
- Classify source types, permitted transformations, retention, and deletion before collection.
- BOUNDARY
- Synthetic share is not treated as a quality metric.
Generation and human audit can be operated as versioned releases
Data without source and transformation history cannot support deletion, correction, or retraining requests.
- APPROACH
- Separate generator and auditor, combining rules, models, and human samples.
- BOUNDARY
- Sources without a deletion path do not enter a release.
05
Design choices
| Decision | Gain | Cost | Watch |
|---|---|---|---|
| Real data cannot sufficiently cover rare behavior and failure conditions | Classify source types, permitted transformations, retention, and deletion before collection. | Synthetic share is not treated as a quality metric. | Source and transformation lineage completeness |
| Generation and human audit can be operated as versioned releases | Separate generator and auditor, combining rules, models, and human samples. | Sources without a deletion path do not enter a release. | Behavior and failure coverage |
Training Dataset Generation: system plate
| Item | Method | Evidence | Boundary |
|---|---|---|---|
| Layer 1 | Classify source types, permitted transformations, retention, and deletion before collection. | Source and transformation lineage completeness | Synthetic share is not treated as a quality metric. |
| Layer 2 | Separate generator and auditor, combining rules, models, and human samples. | Behavior and failure coverage | Sources without a deletion path do not enter a release. |
| Layer 3 | Record PII, domain secrets, and schema errors as independent rejection reasons. | Human-audit rejection rate | Synthetic share is not treated as a quality metric. |
- The workflow begins with Classify source types, permitted transformations, retention, and deletion before collection..
- It reaches an acceptance decision through Source and transformation lineage completeness.
07
Validation plan
| Measure | Method | Pass condition | Caveat |
|---|---|---|---|
| Source and transformation lineage completeness | Classify source types, permitted transformations, retention, and deletion before collection. | Repeated runs satisfy the acceptance threshold agreed during discovery | Synthetic share is not treated as a quality metric. |
| Behavior and failure coverage | Separate generator and auditor, combining rules, models, and human samples. | Repeated runs satisfy the acceptance threshold agreed during discovery | — |
| Human-audit rejection rate | Record PII, domain secrets, and schema errors as independent rejection reasons. | Repeated runs satisfy the acceptance threshold agreed during discovery | — |
08
Constraints and failure conditions
Source rights and sensitive-data boundaries are unverified
Synthetic share is not treated as a quality metric.
The objective is volume without a coverage target
Sources without a deletion path do not enter a release.
09
Engagement model
- 01
Diagnosis
PattyAnalyze the current system and its failure signals.
ClientProvide representative work, data boundaries, and operating constraints.
Source and transformation lineage completeness - 02
Design
PattySeparate generator and auditor, combining rules, models, and human samples.
ClientConfirm owners and acceptance criteria.
Behavior and failure coverage - 03
Validation
PattyRecord PII, domain secrets, and schema errors as independent rejection reasons.
ClientMake the production-transition or stop decision.
Human-audit rejection rate
10
Durable deliverables
- Training Dataset Generation decision record
- Training data is a product with provenance, rejection reasons, and release history—not a pile of examples.Client-owned · Patty-reviewed
- Validation harness and acceptance criteria
- Source and transformation lineage completeness · Behavior and failure coverage · Human-audit rejection rateJointly maintained
- Operations and recovery runbook
- Synthetic share is not treated as a quality metric. · Sources without a deletion path do not enter a release.Operating-team owned
11
Terminology
- Training Dataset Generation
- Design source taxonomy, synthetic generation, independent audit, sensitive-data removal, coverage, and human sampling.
- Acceptance criterion
- Source and transformation lineage completeness
- Operating boundary
- Synthetic share is not treated as a quality metric.
REFERENCES
References and primary material
- Datasheets for Datasets
Primary material for the method and terminology.
- Data Statements
Primary material for the method and terminology.