How confidence is computed
Claro’s confidence score combines four factors:| Factor | Weight | What it measures |
|---|---|---|
| Source reliability | 40% | How trustworthy is the underlying source? First-party documents and authoritative sites score higher than unknown web sources. Knowledge Base content scores higher than open-web search. |
| Content consistency | 30% | Do multiple sources agree? Consistent cross-source signals raise the score; conflicting signals lower it. Single-source answers receive moderate confidence. |
| Model certainty | 20% | How confident was the model in its own output? Low variance in the model’s internal probability distribution indicates higher certainty. |
| Retrieval quality | 10% | How relevant was the retrieved context to the task? Strong semantic match and comprehensive coverage push this higher. |
Confidence bands and their meaning
| Band | Score | What happens |
|---|---|---|
| High | 85–100 | Auto-apply (if at or above the auto-apply threshold). |
| Medium | 60–84 | Typically queued for review. |
| Low | 0–59 | Queued for review or discarded, depending on the reject threshold. |
Three thresholds per operation
| Setting | Effect |
|---|---|
| Auto-apply threshold | Confidence at or above this → the value is written to the record without review. |
| Review threshold | Between auto-apply and this → the value is queued in Notifications for human review. |
| Reject threshold | Below this → the proposed value is discarded with a logged reason. |
Citations
Every auto-applied or queued value carries citations — links to the specific sources the model drew from. Citation types:- Knowledge Base — document name, page number, and the relevant passage.
- Web sources — URL and the extracted text snippet.
- Geolocation — map service and data provider.
- File extraction — document section and page reference.
- Intra-catalogue — another record and attribute on the same or related catalogue.
Working with confidence in practice
Reviewing outputs:- In Notifications, sort review items by confidence ascending to tackle the most uncertain first.
- For large batches, review the bottom decile before raising the auto-apply threshold.
- Add relevant Knowledge Bases — authoritative content is weighted higher than open-web search.
- Write tighter prompts that define the exact format and scope of the answer.
- Provide examples of the desired output.
- Cross-reference multiple sources: configure enrichment to search more than one source and let Claro compare the results.
- A persistently low-confidence field usually means the source data doesn’t cover it, or the prompt is ambiguous. Fix the prompt or the source rather than just lowering the threshold.
- Use the review queue as a training signal: approved values become positive examples that improve future matching in the similarity graph and in human-in-the-loop feedback flows (Dedicated plan).