What is LLM Observability and Monitoring?

Lina Lam· October 17, 2024

Building reliable LLM applications in production is incredibly challenging. You've probably heard of the term. So, what is LLM observability? How does it differ from traditional observability? Why is it so important for production environments?

We will answer these questions for you today. Let's dive in!

What is LLM Observability?

LLM Observability refers to the comprehensive monitoring, tracing, and analysis of LLM-powered applications. It involves gaining deep insights into every aspect of the system, from prompt engineering, to monitoring model responses and user interactions, to evaluating the LLM outputs.

As your product transitions from prototype to production, monitoring key performance metrics helps you to continuously detect anomalies, such as hallucinations and ethical breaches, and fine-tune your model for better performance on the go.

LLM Observability vs. Traditional Observability

LLM observability deals with highly complex models that contain billions of parameters, making it challenging to understand how prompt changes affect the model's behavior.

While traditional observability focuses on system logs and performance metrics, LLM observability deals with model inputs/outputs, prompts, and embeddings.

Another difference is the non-deterministic nature of LLMs. Traditional systems are often deterministic with expected behaviors, whereas LLMs frequently produce variable outputs, making evaluation more nuanced.

In summary:

The Pillars of LLM Observability

1. Request and Response Logging

At the core of LLM observability is the detailed logging of requests and their corresponding responses. Logging them allows you to analyze patterns and understand the context which influenced the outputs.

Monitoring tools typically captures other useful metrics like latency, costs, Time to First Token (TTFT), and more. Tracking conversation histories helps you understand the model's behavior over time.

2. Online and Offline Evaluation

Assessing the quality of the model's outputs is vital for continuous improvement. Defining clear metrics—such as relevance, coherence, and correctness—enables monitoring of how well the model meets user expectations.

Collecting feedback directly from users offers valuable insights, while automated evaluation methods provide consistent assessments when human evaluation isn't practical.

3. Performance Monitoring and Tracing

Once your model's output accuracy reaches an acceptable level, the next thing to focus on should be improving its performance.

For example, tracking latency helps identify any bottlenecks in response generation. Tracking errors such as API failures or exceptions tells you how reliable your AI application is.

Tracing your multi-step workflows helps you debug faster and gives you a deeper understanding of your user's journey.

4. Anomaly Detection and Feedback Loops

Detecting anomalies, like unusual model behaviors or outputs indicating hallucinations or biases, is essential for maintaining application integrity. Implementing mechanisms to scan for inappropriate or non-compliant content helps prevent ethical issues. Feedback loops, where users can provide input on responses, facilitate iterative improvement over time.

5. Security and Compliance

Ensuring the security of your LLM application involves implementing strict access controls to regulate who can interact with model inputs and outputs. Protecting sensitive data requires compliance with regulations like GDPR or HIPAA. Maintaining detailed audit trails promotes accountability and aids in meeting compliance requirements, building user trust.

Why do we need LLM Observability?

• Understand model behavior: Gain visibility into how the model processes inputs and generates outputs.
• Diagnose issues: Quickly identify and resolve errors, bottlenecks, and anomalies.
• Optimize performance: Enhance latency and throughput by fine-tuning model parameters and infrastructure.
• Improve user experience: Tailor responses to meet user expectations and improve satisfaction.
• Ensure compliance: Maintain data privacy and adhere to regulatory requirements.

Best Practices for Monitoring LLM Applications

Deploying LLMs in production comes with its set of challenges. We'll walk through some of the most common ones and how you can address them with Helicone. Let's dive in!

🧠 Use Prompting Techniques to Reduce Hallucinations

LLMs sometimes generate outputs that sound plausible but are factually incorrect - also known as hallucination. As your app usage goes up, hallucinations can happen frequently and undermine your user's trust.

The good news is, you can mitigate this by:

• Designing your prompts carefully with prompt engineering, or
• Setting up evaluators to monitor your outputs in Helicone.

🔒 Preventing Prompt Injections

Malicious users can manipulate their inputs to trick your model into revealing sensitive information or take risky actions. We dive deeper into this topic in the How to prevent prompt injections blog.

On a high-level, you can prevent injections by:

• Implementing strict validation of user inputs.
• Blocking inappropriate or malicious responses.
• Using tools like Helicone or PromptArmor for detection.

⚡ Caching to Improve Performance and Latency

Caching stores previously generated responses, allowing applications to quickly retrieve data without additional computation.

Latency can have the most impact on the user experience. Helicone allows you to cache responses on the edge, so that you can serve cached responses immediately without invoking the LLM API, reducing costs at the same time.

💰 Managing and Optimizing Costs

It’s important to know exactly what might be drilling a hole in your operational cost. LLM monitoring can improve cost savings by tracking expenses for every model interaction, from the initial prompt to the final response.

You can mitigate this by:

• Monitoring LLM costs by project or user to understand spending.
• Optimizing infrastructure and usage.
• Fine-tuning smaller, open-source models to reduce costs.

For more effective cost optimization strategies, check out our blog on 5 Powerful Techniques to Slash Your LLM Costs.

🔄 Continuously Improving the Prompt

As models evolve, it's important to continuously test and audit your prompts to ensure they're performing as expected.

You should experiment with different variations of your prompt, switch models or set up different configurations to find the best performing prompt. You should also evaluate against key metrics that's important to your business.

import OpenAI from "openai";

const openai = new OpenAI({
apiKey: process.env.OPENAI_API_KEY,
baseURL: `https://oai.helicone.ai/v1/${HELICONE_API_KEY}/`
});

Setting Up Real-time Alerts in Helicone

Setting up real-time alerts helps you get instant notifications on critical issues, so that your team can respond quickly and improve the model's responsiveness.

You can configure Slack or email alerts in Helicone to send real-time updates by:

• Defining threshold metrics: Add critical metrics to a watchlist and set thresholds for triggering notification events.
• Monitoring LLM drift: Set up routine reports on key performance metrics to gain insight into model behavioral changes over time.
• Detecting anomalies: Train robust evaluators to identify unusual patterns of behavior.
• Sending notifications: Use webhooks to send alerts to dedicated communication channels

Bottom Line

Now, that you understand how to implement comprehensive LLM monitoring strategies, it’s time to put them into practice. Begin by setting up real-time alerts, tracking key metrics, experiment with different prompts and try out observability tools like Helicone to get more clarity into your LLM app performance.

At Helicone, we're committed to supporting you on this journey. Our solutions are tailored to address the specific needs of LLM applications, empowering you to focus on innovation rather than infrastructure.