2025
Real-Time Quality Feedback Systems
Hallucination Detection & Continuous Learning
AI QualityHallucination DetectionReinforcement LearningMLOps
Context & Problem
Enterprise AI deployments require confidence that outputs are accurate and grounded. Post-hoc evaluation isn't sufficient—quality must be assessed in real-time with continuous feedback to improve model behavior.
Solution & Architecture
Built multi-stage quality pipelines that validate responses against source documents, detect logical inconsistencies, and identify potential hallucinations before they reach users. Reinforcement learning from human feedback enables continuous improvement of response quality.
Key Components
- Multi-layer architecture with clear separation of concerns
- Integration with enterprise systems and data sources
- Scalable infrastructure designed for high availability
- Security and governance built into the core design
Impact
Achieved high-confidence output quality suitable for enterprise deployment, with measurable reduction in hallucination rates and improved factual grounding across diverse query types.
What's Next
- Ontological consistency checking
- Multi-source fact triangulation
- Automated ground truth generation