Model outputs are tracked through three complementary mechanisms focused on ontology quality, dialogue accuracy, and search grounding. For the auto-generated ontology, users review and validate legal concepts through the Galaxy interface, modifying definitions, relationships, or classifications where the AI extraction requires refinement. For dialogue interactions (claimant intake, user queries), the system collects user feedback on response quality and accuracy, allowing continuous improvement of conversational outputs. Search results maintain complete provenance - users can trace any returned information back to source documents, verify grounding, and correct metadata or entity mappings in the Knowledge Fabric if results are inaccurate. All outputs include confidence scores and source references, with low-confidence extractions or contradictory information automatically flagged for human review, creating a transparent feedback loop where users validate and improve AI-generated content rather than accepting it blindly.

Quality adjustments occur at two levels: agent refinement and ontology regeneration, without requiring traditional ML retraining. When metadata quality, entity resolution, or document classification falls short, you can adjust the agents responsible for these operations by modifying their logic, updating confidence thresholds, or refining the extraction patterns they use - without rebuilding the entire Knowledge Fabric. For systemic quality issues, the ontology can be regenerated by ingesting additional or updated legal sources, which enriches the semantic layer with improved legal concepts and relationships. The fabric's monitoring capabilities identify which specific metadata extractions, schema mappings, or entity resolutions are producing low-confidence or inaccurate results, allowing targeted interventions. User feedback on entity corrections, document classification accuracy, and schema validation informs these adjustments, creating a continuous improvement loop. Quality improvements come from ontology enrichment (adding more source documents), agent logic refinement (adjusting extraction rules), and metadata validation workflows - not from retraining machine learning models. Users can build custom agents that monitor quality metrics and automatically trigger corrective actions when thresholds are breached.