# AI Observability in Action: Detect Drift, Stop Hallucinations & Ensure Model Reliability

Erin McMahon

- 13 Aug 2025
- 1 min read

[https://insightfinder.com/wp-content/uploads/Best-Practices-Ideal-Tool-for-Monitoring-LLMs-ML-Models-_-InsightFinder-AI-Observability.mp4](https://insightfinder.com/wp-content/uploads/Best-Practices-Ideal-Tool-for-Monitoring-LLMs-ML-Models-_-InsightFinder-AI-Observability.mp4)

**Curious how top enterprises keep AI models accurate, reliable, and under control?**

In this expert-led webinar, NC State Computer Science Professor and InsightFinder AI CEO **Dr. Helen Gu** and VP of Product **Ciaran Byrne** share proven strategies from leading enterprises—including global banks and credit card providers—on monitoring AI in production and tracing root causes fast.

**What You’ll Learn:**

- Causes of **model drift**, hallucinations, and performance degradation in LLMs

- Why **AI observability** is critical when scaling from experimentation to production

- How InsightFinder AI detects and diagnoses issues before they impact users

- **Root cause analysis** across model, data, and infrastructure layers

- Real-time monitoring for **LLMs** and traditional ML models

- Live demo: **dashboards, monitors, and evaluation tools**

- LLM Labs: test & compare models like GPT-4, Claude, DeepSeek & more

- Enterprise use cases in **finance, cybersecurity, and trading**

Erin McMahon

- Published: 13 Aug 2025
- 1 min read