LLM Monitoring

LLM Monitoring: A Guide to Evaluating AI Applications In Production

How do you know when your LLMs aren’t performing as you intended? When operating at scale and interacting with thousands of users, how do you make sure the quality stays high and the AI doesn’t make embarrassing mistakes?

LLM monitoring is a technique used to solve this by measuring the performance of an AI applications continuously and at scale. This blog explains what it is and why it’s important, as well as best practices and important metrics you should know.

What is LLM Monitoring?

LLM monitoring is the ongoing process of observing and analyzing the performance of a large language model (LLM) once it is deployed in production environments. It’s akin to having a watchful eye on a complex machine, ensuring it operates efficiently and produces the desired outcomes.

To ensure optimal performance and reliability, LLM monitoring focuses on several key areas:

• Model performance: Tracking metrics like accuracy, latency, and response time to assess the model's effectiveness.
• Data quality: Evaluating the quality and relevance of the data used to train and tune the model.
• Bias detection: Identifying and mitigating biases in the model's outputs to ensure fairness.
• System performance: Monitoring the underlying infrastructure and resources to prevent bottlenecks.
• User experience: Gathering feedback on user interactions to improve the model's usability.

By systematically tracking these areas, organizations can identify potential issues early, prevent service disruptions, and continually improve LLM performance.

Why is LLM Monitoring Important?

Deploying an LLM into the real world is not without its risks - there are underlying vulnerabilities and uncertainties in how the models will behave. Despite their sophistication, LLMs are prone to errors and performance fluctuations which can escalate without vigilant monitoring. Some examples would be:

• Reputational Risk: LLMs can generate unexpected or harmful outputs. Monitoring helps identify and mitigate these risks before they cause damage. For instance, an LLM tasked with generating creative marketing copy might unexpectedly produce content that is offensive, potentially damaging the brand's reputation.
• Performance Degradation: Over time, LLMs can experience prompt drift, where the model's responses deviate from the expected outputs. Monitoring helps identify these drifts early, allowing for timely adjustments to keep things on track. This is important in dynamic environments where the nature of the prompts can change rapidly, for example if your application is heavily reliant on user input for direction. Furthermore, if you’re using a model alias (such as gpt-4o), which automatically updates to the latest version of the model, you’re more prone to prompt drift.
• Compliance Breaches: Monitoring ensures that the data processed by the LLMs is handled correctly and in compliance with regulatory standards. This is critical for industries dealing with sensitive information, such as healthcare, where data breaches can have severe consequences. The EU specific rules around this, which you can check out in guide on the EU AI Act.

Best Practices for LLM Monitoring

Navigating the complexities of LLM production requires a strategic approach to monitoring. Here are some best practices to follow:

• Track Behavioral Metrics: LLM monitoring should capture not just functional performance data– cost, latency, and error rates– but also the model’s inputs and outputs. By logging both the raw data fed into the model and the results it generates, teams can better understand patterns of success and failure.
• Make Monitoring Data Accessible: Monitoring logs shouldn’t be confined to technical teams alone. Product managers, engineers, and other stakeholders should have easy access to this data to support decision-making. Sharing this data across teams helps ensure that both functional issues (like bugs or performance bottlenecks) and strategic challenges (like alignment with user needs) are promptly addressed.
• Incorporate Diverse Sources of End-user Feedback: LLMs interact with users in a variety of ways, so it’s crucial to capture feedback through multiple channels. Collect structured feedback like user ratings, implicit feedback based on user behaviour, and unstructured feedback in the form of natural language comments.
• Automate Subjective Evaluations with LLM-based Tools: Use automated LLM evaluators to scale the assessment of subjective metrics such as tone, coherence, or the presence of hallucinations. These tools can help flag issues like inappropriate responses or factual inaccuracies more consistently than manual reviews, enabling continuous improvement at scale.

LLM Monitoring vs Evaluation

The main difference between LLM monitoring and evaluation is that monitoring has to happen live (online) and evaluation happens during development (offline) and when there are major changes being made to model’s configuration. Let's break down the key differences.

Monitoring

LLM monitoring is a real-time process focused on tracking model performance as it operates in production. It uses automated tools to spot issues like regressions, errors, or performance bottlenecks without relying on expensive, manual interventions. Since monitoring happens live, it needs to be efficient and cost-effective, often relying on system-level metrics.

The metrics used may include:

• Latency and Response Time: How quickly the model generates outputs.
• Error Rate: Frequency of incorrect or failed responses.
• User Interaction Metrics: Monitors user engagement and satisfaction.
• Token Utilization: Observes and tracks average tokens used per generation.

Monitoring is about maintaining operational stability, quickly identifying anomalies, and gathering data for long-term optimization. As a result, monitoring typically has to be low cost and fully automated. The goal of monitoring is to spot regressions, get alerted to issues, and to have data to improve things over time.

Evaluation

Evaluation, on the other hand, is a periodic process that assesses the model's overall effectiveness and quality. It is more in-depth and offline, and is often conducted after major changes or during development cycles. Unlike monitoring, evaluation can involve more resource-intensive methods such as manual review or LLM-as-a-judge to measure the LLM’s effectiveness across various quality dimensions.

Common methods for evaluation include:

• Heuristic/Code: Using simple deterministic rules based on judgments against attributes like cost, token usage, latency, regex rules on the output, etc. These are generally fast and cheap to run at scale.
• Model (or 'AI'): Using other foundation models to provide judgments on the output of the component. This allows for more qualitative and nuanced judgments for a fraction of the cost of human judgments.
• Human: Getting gold standard judgments from either end users of your application, or internal domain experts. This can be the most expensive and slowest option, but also the most reliable.

Key metrics can vary depending on the return type being used:

• Classification: Precision, Recall, and F1.
• Ranking: NDCG, Elo ratings, and Kendall's Tau.
• Numerical: Bleu score, a percentage, or any other kind of numeric rating.

Evaluation is less about immediate performance and more about ensuring guiding long-term improvements. Read our guide on evaluating LLM applications to learn more.

How to Choose the Right LLM Monitoring Metrics

Choosing the right metrics for LLM monitoring depends on the specific requirements and goals of your application. Take a look at the key criteria to evaluate, with corresponding metrics that help ensure your model is functioning optimally in production.

Cost

Managing cost is critical, particularly for large-scale deployments. Monitoring cost-related metrics helps balance resource consumption without sacrificing performance.

• Inference Cost per Query: Track how much each LLM query costs in terms of cloud, infrastructure or API usage.
• Token consumption: Track the number of tokens used per query to optimize computation resources.
• Scalability Costs: Measure how costs scale as user load increases, identifying potential inefficiencies.

Latency

Latency, or the time it takes for the model to generate a response, directly impacts user experience. High latency can degrade user satisfaction, especially in real-time applications.

• Response Time: Measure the time between input submission and output generation.
• Time to First Token (TTFT): Measure how quickly users begin to see the model's output after entering their query. Low TTFT is critical for real-time interactions.
• Time Per Output Token (TPOT): Track the time it takes to generate each output token. For example, a TPOT of 100 milliseconds/token translates to ~450 words per minute, offering insight into how users perceive the model’s speed.
• Throughput: Monitor the number of successful queries processed per second, ensuring the model scales under load.

Accuracy

Accuracy measures how correct the model's outputs are, particularly in critical applications like customer service, legal advice, or data analysis.

• Precision and Recall: Measure the relevance and completeness of responses in information retrieval tasks.
• Confusion Matrices: Break down where the model gets predictions right or wrong across different categories.
• Task-Specific Metrics: For language tasks like summarization, translation, or question answering, use industry benchmarks such as BLEU, ROUGE, or METEOR to measure text quality.

Helpfulness

Helpfulness evaluates how well the model assists users in achieving their goals, often subjective and user-specific.

• User Feedback Scores: Collect structured feedback from users (e.g., thumbs-up/down, ratings) to gauge how helpful responses are.
• Task Completion Rate: Track whether the model helps users complete tasks successfully (e.g., making a booking or finding relevant information).
• Interaction Duration: Measure whether users spend too much time interacting with the system to get a useful answer, which could indicate a lack of helpfulness.

Structure

Structured responses ensure the model provides clear, well-organized information, especially in business or technical applications.

• Grammatical Accuracy: Use automated tools to assess grammar and coherence.
• Response Format Adherence: Monitor whether the model adheres to predefined formats (e.g., bullet points, paragraphs, or numbered lists).
• Response Length: Ensure that outputs are neither too short nor unnecessarily long, maintaining conciseness while providing full context.

Tone of Voice

Tone of voice consistency can be vital for applications involving customer service, content generation, or professional communication.

• Tone Consistency: Monitor outputs using LLM-based evaluators or sentiment analysis tools to ensure the model maintains a consistent tone (e.g., formal, neutral, friendly).
• User Sentiment Feedback: Collect subjective feedback from users to ensure the model's tone matches expectations.

Note on Performance Benchmarks:

While performance benchmarks like BLEU, ROUGE, and METEOR are useful for assessing the technical accuracy of language models, they often fall short in capturing the subjective nuances required for real-world use cases. These benchmarks measure aspects like grammar, syntax, or factual correctness, but they don't fully account for subjective qualities like tone and helpfulness, or alignment with business goals.

LLM-based evaluation (or llm-as-judge) can be used to evaluate these subjective components of model outputs in a manner that’s scalable, customizable, and context-aware. For most modern generative AI applications, LLM-as-a-judge is essential to the development of prompts that are reliable at scale.

LLM Monitoring Challenges

Managing Anomalies

LLMs can generate unexpected or harmful outputs, which may go unnoticed without proper monitoring. Deploying anomaly detection algorithms and robust alerting systems can help identify and address these issues promptly. Regularly updating the model to handle new types of inputs and scenarios also enhances its robustness against unexpected outputs.

Handling Performance Regressions

As underlying models are updated or when a new model is released, the expected performance of a given prompt may change. This risk here can be mitigated by evaluating the performance of prompts using production data and comparing performance before deploying a model update.

Interpretability and Explainability

Understanding why a model made a specific prediction or generated certain text is often difficult due to the black-box nature of LLMs. Diagnosing issues like incoherent responses, hallucinations, or irrelevant answers requires detailed logging and analysis, which may be challenging at scale.

Learn More About LLM Monitoring

Humanloop is the LLM evaluations and monitoring platform for enterprises. Engineering and product teams at Gusto, Vanta, and Duolingo use Humanloop to ship AI products that are reliable at scale.

To learn how you can adopt best practices for prompt management, evaluation, and observability, book a demo today.