AI and LLM Observability

In my 15 years of building AI products, I’ve witnessed firsthand the transformative power—and inherent risks—of deploying large language model (LLM) apps in production.

Let me bring you through some key insights and best practices for monitoring these systems so that you can keep your users trust in your product.

TL;DR: Trust Is Hard Earned and Easily Lost

Building user trust in an LLM-enabled product is the only thing that matters for engagement and revenue.

Users come to expect that every interaction will be seamless, accurate, and contextually relevant. When you consistently deliver on these promises, you earn their trust bit by bit. But the moment a response is off, misleading, or just plain irrelevant, that trust can evaporate almost instantly.

This isn’t a one-time achievement—it’s a continuous, meticulous process. Real-time monitoring, constant quality evaluations, and rapid response to anomalies is essential.

How is user experience (UX) related to LLM observability?

User expectations for LLM products are evolving at breakneck speed. Early on, a polished, responsive app sets the baseline. But as users become more familiar with AI capabilities, they start demanding more nuance, accuracy, and personalization from every interaction. Over time, if your monitoring and optimization processes aren’t keeping pace, the user experience (UX) can start to degrade.

There are several reasons why UX may regress over time:

• Model Drift and Data Changes: As the underlying data or model parameters shift—even subtly—the quality of responses can deteriorate, leading to inconsistent or outdated outputs.
• Feature Creep and Interface Complexity: Adding new features without careful UX planning can clutter the interface, confuse users, and dilute the effectiveness of core functionalities.
• System Performance Variability: Without continuous performance tuning, latency may increase or errors may start creeping in, causing frustration for users who once experienced a smooth interaction.

This is a AI/LLM guide, so lets focus on model drift and data changes.

What to monitor for LLM applications?

User feedback: User feedback provides the more critical layer of insight. Add buttons and forms in your UI to let users flag responses, give ratings, and submit comments. Comparing this feedback with quantitative metrics gives you a complete view of model drift, helping you make targeted improvements.

After that, we should track some more metrics.

Inputs: Begin by tracking the statistical properties of inputs. Anomaly detection techniques, such as KL divergence or KS tests, can help you quickly identify shifts away how you assumed the inputs would look like.

Outputs: Gauge the accuracy and relevance of your model’s responses with technical metrics like perplexity, BLEU scores. The key here is to establish metrics custom to your use case, see our AI evaluation guide for more details.

Baselines: Establishing baselines for both input and output metrics and conducting temporal trend analysis using rolling averages and time-series analysis will enable you to detect gradual performance degradation. This proactive approach can alert you to subtle shifts before they affect the user experience.

Data pipeline: Your data pipeline is just as crucial as your model. Keeping an eye on the freshness and consistency of incoming data ensures that your model isn’t learning from outdated or corrupted sources. By setting up automated checks, you can be alerted when data quality degrades, allowing you to fix issues before they affect users.

How do I monitor LLMs?

1. Continuous Evaluation of Output Quality

Let me bring you through the concept of quality monitoring. When an LLM app is running in production, its outputs may change due to updates in data, shifts in user behavior, or model drift. To counteract this, I always advise establishing an evaluation framework that includes:

• Automated Scoring Systems: Implement real-time scoring to assess the accuracy, coherence, and relevance of responses.
• Feedback Loops: Integrate user feedback directly into your monitoring system. Even a simple thumbs-up or down can signal when the model’s performance is deviating from expectations.
• Regression Tests: Periodically run batch evaluations against a “golden dataset” of known inputs and outputs to catch subtle changes before they escalate.

2. System Health and Resource Utilization

When looking at this problem, you would want to think about the underlying infrastructure too. Monitoring isn’t just about what the LLM outputs—it’s also about ensuring your system runs smoothly:

• Latency & Throughput Metrics: Track the time taken for responses and the volume of queries processed. Spikes in latency or drops in throughput might indicate underlying issues that need immediate attention.
• Resource Consumption: Keep an eye on CPU, GPU, and memory usage. Efficient resource management not only controls costs but also prevents performance bottlenecks.
• Error Tracking: Implement comprehensive logging for prompt-response pairs and system errors. Detailed logs help pinpoint the root cause of issues, whether they stem from the model or integration layers.

3. Observability and Governance

In my experience, maintaining trust isn’t just about technical metrics—it’s also about governance. With the regulatory landscape tightening around AI, especially for models processing sensitive data, you must:

• Establish Clear Governance Policies: Define what “good” output means for your use case and set thresholds for performance and safety.
• Implement Alerting Mechanisms: Use real-time dashboards and alerts to notify your team when key metrics deviate from expected ranges.
• Data Privacy and Security: Ensure that your monitoring processes comply with data protection regulations. This not only safeguards your users but also builds long-term trust in your brand.

Best Practices for CTOs and Devs

Start Simple, Then Scale

In my experience, it’s best to begin with a simple monitoring system and gradually add layers of complexity. For example, start by logging basic metrics such as response times and error rates. Once you have a solid foundation, integrate more sophisticated measures like automated quality evaluations and predictive alerts.

Integrate Monitoring into Your CI/CD Pipeline

When looking at this problem, you would want to think about continuous integration and continuous deployment (CI/CD). By embedding monitoring into your CI/CD pipeline, you ensure that every update, no matter how small, is automatically tested and validated against your quality benchmarks before reaching users.

Collaborate Across Teams

Successful monitoring is not solely an engineering task. It requires close collaboration between data scientists, engineers, product managers, and even customer support teams. Regular cross-functional reviews of monitoring dashboards can uncover hidden issues and provide insights on how to improve both model performance and user experience.

Final Thoughts

Deploying LLM-enabled apps in production is an exciting frontier, but with great power comes great responsibility. In my experience, the key to maintaining user trust lies in robust, continuous monitoring and an agile approach to problem-solving. When you know what metrics to watch, integrate feedback loops, and build a system that’s both reliable and transparent, you create a foundation of trust that will propel your AI solutions to new heights.

Let’s continue to innovate responsibly—after all, the future of AI isn’t just about achieving technical excellence, but also about earning and maintaining the trust of every user who interacts with our creations.

Feel free to reach out with your thoughts or share how your team is tackling LLM monitoring challenges. Together, we can ensure that the promise of AI continues to inspire confidence and drive progress.