SAG / ARCHITECTURE NOTE
Golden Evaluation Sets: How Do You Regression-Test Improvements to Automated Analysis?
Evaluation data for comparing the effects of analysis changes by fixing reviewed questions, evidence, and expected judgments. If you change a model or extractor and check only for polished answers, past errors may return. Search, generation, and metric errors must be measured separately.
What Is a Golden Evaluation Set?
It is evaluation data for comparing the effects of analysis changes by fixing reviewed questions, evidence, and expected judgments. This note considers golden evaluation sets in terms of the responsibilities of their inputs, transformations, and outputs, rather than as a feature name. To trust analysis results, there must be a traceable connection between what data was provided, what was checked, and how far the conclusions can go.
Why Is This Technology Needed?
If you change a model or extractor and check only for polished answers, past errors may return. Search, generation, and metric errors must be measured separately.
Design Principles and Data Flow
Include clear positive cases, missing-data cases, and counterexamples, and evaluate evidence alignment separately from calculation and source accuracy. Approved, de-identified data can be used instead of original customer content.
Reviewed questions and evidence → Fixed expected judgments → Regression and expert review
Each stage must not relabel the success of the preceding stage as the performance of the next. Recording data identifiers, time periods, and validation status together makes it possible to locate where omissions and errors occurred and determine what needs to be checked again.
Connection to the SAG Architecture
SAG regression-tests citation completeness, duplicates, permissions, time periods, and metric calculations. Extending model-based semantic evaluation requires foundational checks and a separate evaluation set.
SAG’s operational value lies in connecting these relationships to pages and questions, comparison results, and improvement tasks. Rather than reading only the numbers, customers can review what needs to be strengthened along with the basis for the judgment. Patterns that require further application should be interpreted according to the scope of the relevant paragraph.
Illustrative Example and Judgment Criteria
In the illustrative case where the same URL is cited twice in one answer, the expected count is 1. An unverified list is excluded, not counted as 0. Counterexamples like these are needed to find errors that simple positive examples would miss.
The example above is provided to explain structure and calculation; it is not measured performance from a particular customer. Actual reports must link the selected period, subject, observation conditions, and original records so that the same judgment can be checked again.
Practical Validation Checklist
| Flow stage | Items to check |
|---|---|
| Reviewed questions and evidence | Include positive cases, missing-data cases, and counterexamples |
| Fixed expected judgments | Separate search, generation, and calculation |
| Regression and expert review | Preserve evaluation versions and errors |
Check whether the same meaning is maintained not only with normal inputs, but also with empty, duplicate, and differently conditioned data. Connecting validation items to completion criteria can reduce the gap between feature descriptions and actual operations.
Limitations and Application Considerations
Scores from automated evaluators do not guarantee correctness either. Preserve expert reviews, misjudgment cases, evaluation versions, and sample scope together.
Research and Official Documentation
- RAGAs evaluation research — Research evaluating retrieval and generation results; check the purpose and limitations of its scores.
External materials provide background on the design topics above; they do not certify every SAG implementation or customer outcome. Interpretations of this note and its illustrative examples are organized around SAG’s operational structure. Materials checked: 2026-10-06.
Further Reading and Feature Review
- Related architecture note
- Try a service connected to the golden evaluation set
- Feature-specific FAQ
- Discuss implementation scope
How to Continue Reading About This Technology
Read about the problems that SEO, AEO, GEO, entities, and JSON-LD each address.
SAG / KNOWLEDGE LINKS