Update JSON output

Author: Additi

articles/ai-services/content-understanding/Confidence Score

@@ -19,10 +19,25 @@ Confidence scores represent the probability that the extracted result is correct
 Why are confidence scores important?
 Confidence scores are important because they provide a measure of the model's certainty in its predictions. They help users make informed decisions about the reliability of the extracted data and determine whether human review is necessary. Confidence scores also play a crucial role in automating workflows and improving efficiency by reducing the need for manual validation.
 Supported fields
-Confidence scores are supported for extractive various fields, including text, tables, and images. The specific fields supported may vary depending on the model and the use case.
+Confidence scores are supported for extractive fields, including text, tables for documents and speech transcription. The specific fields supported may vary depending on the model and the use case.
 
+JSON output for documents
+"fields": {
+					"ClientProjectManager": {
+						"type": "string",
+						"valueString": "Nestor Wilke",
+						"spans": [
+							{
+								"offset": 4345,
+								"length": 12
+							}
+						],
+						"confidence": 0.964,
+						"source": "D(2,3.5486,8.3139,4.2943,8.3139,4.2943,8.4479,3.5486,8.4479)"
+					},
 What are thresholds for confidence scores?
 Thresholds for confidence scores are predefined values that determine whether a prediction is considered reliable or requires further review. These thresholds can be set across different modalities to ensure consistent and accurate results. Setting appropriate thresholds is important because it helps balance the trade-off between automation and accuracy. By setting the right thresholds, users can ensure that only high-confidence predictions are automated, while low-confidence predictions are flagged for human review. This helps improve the overall accuracy and reliability of the predictions
+
 Improving Confidence Scores
 What are some common challenges with confidence scores?
 Common challenges with confidence scores include low-quality input documents, variability in document types, complexity of the documents, and limitations of the model in recognizing certain types of content or features.