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AI and LLM Observability for Enterprise IT

Organizations across all industries are ramping up efforts to integrate AI using large language models (LLMs) into their IT infrastructure. While the technology is poised to improve operational efficiency, the customer experience, work quality, and overall stack performance, it does create challenges.

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For LLMs to effectively do their job and drive positive business outcomes, outputs must be current, accurate, unbiased, and highly relevant to your specific business, without inviting too many security risks. Ensuring that requires consistent real-time LLM monitoring, and that’s often where organizations struggle.

Massive amounts of data, coupled with a more complex tech stack make LLM monitoring more difficult than traditional systems monitoring. Managing the whole process can become a headache for IT or AIOps if they don’t deploy an LLM observability solution, such as BMC Helix AIOps, which is designed to specifically manage the data volume, variety, and velocity of today’s AI/LLM-powered organizations.

First, consider the challenges of manual monitoring:

  • It’s nearly impossible to identify issues fast enough to prevent performance or security problems.
  • It requires significant time and human resources.
  • Human monitoring is prone to errors and missed problem detection.
  • Manual processes can’t easily or effectively scale as the IT infrastructure expands.

Instead, a sophisticated generative AI (GenAI) observability solution can be an extension of your current observability practices, offering you end-to-end visibility across your LLM systems, as well as thorough LLM monitoring and LLM tracing.

What is AI and LLM observability for enterprise IT?

AI and LLM observability is the real-time process of monitoring, tracing, and analyzing systems that are powered by AI using LLMs. AI and LLM observability collects data from AI/LLM apps to gain insights into the application and LLM outputs. It ultimately enables you to understand how your AI/LLM applications behave and how their performance ebbs and flows. LLM monitoring is vital to detecting issues, such as security threats, hallucinations, and biases, so you can resolve them before they negatively impact the business or user experience.

How is LLM observability different from traditional application observability?

Non-predictability

LLMs are highly non-predictable. They are consistently producing different outcomes, even with the exact same inputs or prompts. In fact, highly complex LLMs contain billions of parameters that drive how an app both understands inputs and acts on them.

So while traditional systems typically exhibit expected behaviors and are more predictable, LLMs evolve as the data evolves. Because of that, troubleshooting and performance monitoring are more complex, and traditional application observability won’t provide you with the range or depth of insights needed for effective LLM monitoring.

Model-focused monitoring

Traditional application observability focuses on system logs and metrics (i.e., request latency, CPU usage, memory consumption, and request latency). LLM observability goes further to monitor factors specific to the model, for example, prompt inputs and outputs, token usage, and the behavior of the model over time.

Traditional application observability can tell you if an app is running efficiently, for example, or detect problems, such as crashes or latency issues. LLM observability will tell you why the model output failed, reveal causes of errors, and track issues, such as hallucinations, drift, or bias.

LLM observability monitors the complete prompt-response cycle, offering end-to-end visibility from the moment a user first interacts with an LLM until there is no further interaction between the user and the LLM.

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Which key signals should be monitored for LLMs?

In LLM observability, signals, or performance metrics, indicate how the LLM is performing, the overall quality of the model, and the cost to the organization. They enable the organization to identify weaknesses and fix problems. Here are several signals the most powerful LLM observability solutions monitor:

How do traces, logs, and metrics change when observing AI and LLM-powered services?

In LLM observability, metrics, logs, and traces function similarly as they do in traditional observability. However, LLM observability can also manage the challenges of non-deterministic LLM-powered applications.

How does AI/LLM observability integrate with existing observability and AIOps platforms?

It’s important to understand that large language model observability is not a replacement for your existing observability processes and solutions. Instead, see it as an enhancement to your existing observability and AIOPs stack that will enable more robust LLM performance monitoring. 

The most effective LLM monitoring or LLM observability tools will seamlessly integrate within your current tech stack, granting you more and deeper insights into your LLM-powered systems and applications. 

Leading LLM observability tools, such as BMC Helix AIOps, fully integrate with existing systems by ingesting data, including metrics, traces, logs, incidents, changes, topology, and events from a wide range of native and third-party solutions. 

The solution will then provide a single pane of glass offering end-to-end visibility across ever-evolving LLM systems. Intelligent automations then sync with other automation tools, which makes it possible for organizations to proactively identify, detect, and prevent issues, while building more reliable LLMs overtime.