What is LLM Observability?
Since the release of Large Language Models (LLMs) like GPT, LaMDA, LLaMA, and several others, generative AI has quickly become more integral to aspects of business, finance, security, research, and language than ever before. For example, Microsoft launched LLM-powered Copilot for improved office productivity in everyday tasks and Stripe collaborated with Open AI to manage frauds and increase conversion rates.
While the adoption of LLMs has experienced a significant boost, running LLM applications in production has proven to be more challenging compared to traditional ML applications. This difficulty arises from the massive model sizes, intricate architecture, and non-deterministic outputs of LLMs. Furthermore, troubleshooting issues originating in LLM applications is a time-consuming and resource-intensive task due to the black-box nature of their decision-making processes. Hence, managing LLMs requires continuous monitoring to guarantee sustained performance and security while producing precise and unbiased responses.
LLM observability provides tools, techniques, and methodologies to help teams manage and understand LLM application and language model performance, detect drifts or biases, and resolve issues before they have significant impact on the business or end-user experience.
What are the common issues with LLM applications?
As artificial intelligence and LLM tools are in their infancy, there are a number of issues that can occur, both prompted by users and within the LLM’s responses. A proper LLM monitoring tool can help organizations keep track of issues that might occur with LLM applications, such as:
Hallucinations: LLM powered applications may occasionally produce false information, a phenomenon referred to as “hallucinating”, particularly when confronted with queries for which they do not possess an answer. Rather than acknowledging their lack of knowledge, they frequently yield responses that may appear assured but are essentially flawed. This propensity can potentially foster the spread of inaccurate information, a consideration that is crucial when applying LLMs to activities that necessitate factual accuracy.
Performance and cost: Often the applications built using LLMs rely on third-party models. This dependence can lead to issues such as performance degradation of third-party APIs, inconsistencies due to changes in their algorithms, and high costs—especially with large data volumes.
Prompt hacking: Prompt hacking, or sometimes referred to as prompt injection, is a technique where users can influence LLM applications to produce specific content. This manipulation can potentially cause LLMs to generate inappropriate or harmful material. Awareness of this issue is vital, particularly when deploying LLMs in customer-facing applications.
Security and data privacy: LLMs pose security issues, including potential data leaks, output biases due to skewed training data, and risks of unauthorized access. Additionally, LLMs may generate a response containing sensitive or personal data. Thus, stringent security measures and ethical practices are vital with LLMs.
Model prompt and response variance: The user prompts received by LLMs and the responses they generate vary in attributes such as length, language, and accuracy. Users may also receive different responses to the same query, which may lead to confusion and inconsistent user experience. This enforces the need for continuous monitoring and logging of LLM applications.
What are the benefits of LLM observability?
As LLM tools rapidly evolve, organizations that implement in-depth monitoring of their applications can expect the following benefits:
Improved LLM application performance: LLM observability enables real-time monitoring of various performance evaluation metrics such as latency and throughput of LLM applications and quality of responses. By continuously monitoring these metrics, data scientists and engineers can quickly identify any deviations or degradation in LLM performance. This proactive approach allows for timely intervention, leading to improved model performance and user experience.
Better explainability: With LLM observability, you can get deep model insights into the inner workings of LLM applications. By visualizing the request-response pairs, word embeddings, or prompt chain sequence, LLM observability enhances the interpretability of responses. This increased transparency enables stakeholders to trust LLM applications’ decisions and identify any quality issue or errors in the application’s outputs and logic.
Faster issue diagnosis: End-to-end visibility into the operation of an LLM application is essential to resolve issues such as no or incorrect responses. LLM observability enables engineers to analyze the backend operations and API calls for a request to pinpoint the root cause of an issue, reducing the time it takes to resolve the issue.
Increased security: LLM observability plays a crucial role in enhancing the security of LLM applications by monitoring model behaviors for potential security vulnerabilities or malicious attacks. By tracking access patterns, input data, and model outputs, LLM observability tools can detect anomalies that may indicate data leaks or adversarial attacks. This continuous monitoring helps data scientists and security teams proactively identify and mitigate security threats, safeguarding sensitive data and maintaining the integrity of LLM applications.
Efficient cost management: Observing the resource consumption and utilization of LLM models allows organizations to optimize resource allocation and cost based on actual usage patterns. By monitoring metrics such as token consumption, CPU/GPU utilization, and memory usage, observability tools help in identifying resource bottlenecks or underutilization. These insights can inform decisions on scaling resources up or down, ensuring cost-effectiveness of LLM applications.
What should you look for in an LLM observability solution?
When considering a solution that supports AI observability for generative AI and language models, review these features offered by LLM monitoring tools:
LLM chain debugging: A majority of modern LLM applications are built by logically chaining LLM agents together where output from one is fed as input to another before returning the final output to the user. This can make it hard to understand why an LLM agent is looping or why a chain is slower than expected. Hence it is essential that the LLM monitoring tool provides the visibility into the complete operation of LLM chains for swift troubleshooting and issue resolution.
Visibility into complete application stack: The symptoms of the issues happening in the backend of LLM applications appear at the user interface of the application. For efficient troubleshooting, it is essential to know which element of the LLM application stack (GPU, database, service, or model) failed upfront.The scope of your LLM observability solution should cover the entire application stack and areas relevant to your needs.
Explainability and anomaly detection: An ideal LLM observability solution should provide insights into the decision-making process of AI models, promoting transparency and explainability. Additionally, the solution should have out-of-the-box capability to effectively monitor and analyze data inputs and outputs to detect anomalies, biases, and user feedback.
Scalability, integration, and security: LLM monitoring solutions should be highly scalable to handle increasing user workloads while seamlessly integrating with various LLM platforms used by applications. It must provide robust security features including PII redaction, sensitive data scanning, and protection against prompt hacking.
Full lifecycle support: While LLM observability is essential for smooth LLM operations in production, it also plays a key role in experimenting and fine-tuning of models during development phrases.
Learn more about LLM observability
Datadog offers generative AI observability capabilities to allow teams to deploy LLM-based applications to production with confidence and help troubleshoot health, cost, and accuracy in real time. From OpenAI’s GPT models to Amazon Bedrock, Datadog’s LLM monitoring not only tracks usage metrics but also provides insight into how models perform, alerting organizations to issues such as prompt hijacking, unexpected results, and more. Review Datadog’s integrations for AI and LLM technologies to learn more about all of the LLM tools Datadog supports.
