Best Practices for Monitoring Large Language Models

Large Language Models (LLMs) are powerful tools for natural language processing (NLP), but they can also present significant challenges when it comes to monitoring their performance and ensuring their safety. With the growing adoption of LLMs to automate and streamline NLP operations, it's crucial to establish effective monitoring practices that can detect and prevent issues. Simply relying on embeddings is no longer enough in today's landscape!

In this post, we'll share some best practices for LLM monitoring, including selecting appropriate metrics, establishing reliable alerting systems, and ensuring scalability for your monitoring practices.

Get started with LangKit with just a few lines of code and start monitoring your LLM today! If you want more details about implementing guardrails, evaluation and observability - you can read more about it here.

Choosing the right LLM monitoring metrics

One of the first things to consider when implementing LLM monitoring is choosing the right metrics to track. While there are many potential metrics that can be used to monitor LLM performance, some of the most important ones include:

• Quality: Are your prompts and responses high quality (readable, understandable, well written)? Are you seeing a drift in the types of prompts you expect or a concept drift in how your model is responding?
• Relevance: Is your LLM responding in with relevant content? Are the responses adhering to the topics expected by this application?
• Sentiment: Is your LLM responding in the right tone? Are your upstream prompts changing their sentiment suddenly or over time? Are you seeing a divergence from your anticipated topics?
• Security: Is your LLM receiving adversarial attempts or malicious prompt injections? Are you experiencing prompt leakage?

Simplify your language model monitoring with WhyLabs' LangKit - the open-source text metrics toolkit that lets you extract important telemetry with just a prompt and response. Then with the WhyLabs platform you can easily track this telemetry over time and enable collaboration across teams without the need to set up or scale any infrastructure.

Setting up effective alerting systems

Once you've chosen the right metrics to track, the next step is to set up effective alerting systems that can help you quickly identify and respond to potential issues. Some key considerations for effective alerting systems include:

• Thresholds: Set thresholds for each metric that trigger an alert when breached. For example, you might set a threshold for sentiment that triggers an alert when it falls below a certain percentage.
• Frequency: Determine how frequently you want to check each metric and set up alerts accordingly. For critical metrics, such as jailbreak similarity or throughput, you may want to check them more frequently than less critical metrics.
• Escalation: Establish a clear escalation path for alerts, so that you can quickly involve the right people or teams when necessary. This might involve setting up a hierarchy of alerts, or establishing clear protocols for responding to alerts.

WhyLabs makes it easy to establish thresholds and baselines for a range of activities - including malicious prompts, sensitive data leakage, toxicity, problematic topics, hallucinations, and jailbreak attempts. With alerts and guardrails, application developers can prevent unwanted LLM responses, inappropriate prompts, and policy violations - no NLP expertise required!

Ensuring reliability and scalability

Finally, it's important to ensure that your LLM monitoring practices are both reliable and scalable. Some key tips for achieving this include:

• Automate as much as possible: Use automation tools to streamline the monitoring process and reduce the risk of human error. This might involve setting up automated scripts or workflows that check metrics and trigger alerts.
• Use cloud-based solutions: Consider using cloud-based solutions for LLM monitoring, as these can provide greater scalability and flexibility than on-premise solutions.
• Monitor the monitoring: Don't forget to monitor your monitoring practices themselves, to ensure that they are working as intended. This might involve setting up additional checks or audits to verify that alerts are being triggered correctly, or that metrics are being tracked accurately.

Implementing large language model monitoring

Monitoring LLMs in production is a critical task for organizations that want to ensure the reliability, safety, and effectiveness of their NLP operations. By following the best practices outlined in this post, you can help ensure that your LLM monitoring practices are both effective and scalable, and that your organization is well-positioned to take advantage of the many benefits that LLMs can offer.

Safeguard your Large Language Models with LangKit

After working with the industry's most advanced AI teams to develop a solution to make LLMs safe and reliable, we developed LangKit to enable AI practitioners to identify and mitigate malicious prompts, sensitive data, toxic responses, hallucinations, and jailbreak attempts in any LLM model! Easily set up the key operational processes across the LLM lifecycle:

• Evaluation: Validate how your LLM responds to known prompts both continually as well as ad-hoc, to ensure consistency when modifying prompts or changing models.
• Guardrails: Control which prompts and responses are appropriate for your LLM application in real-time.
• Observability: Observe your prompts and responses at scale by extracting key telemetry data and compare against smart baselines over time.

Get started with LangKit with just a few lines of code and incorporate safe LLM practices into your projects today!