Why You Should Monitor Your AI Applications (Part 1)

Lina Lam· March 13, 2025

Today, AI-powered applications are customer-facing, handle sensitive information, make important decisions, and quite frankly, represent your brand.

With tools like v0 and cursor, it's easier than ever to spin up an LLM app, but it's harder than ever to build reliable and production-ready LLM applications. At Helicone, we believe that effective monitoring is now a competitive advantage.

In this first guide of our two-part series, we'll explore:

• What is LLM observability?
• How it's different from traditional observability
• The key metrics you should be tracking
• The five pillars of comprehensive LLM monitoring
• Why observability is no longer optional

Let's dive in!

What is LLM Observability?

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

Unlike traditional software where you can trace through deterministic code, LLMs operate as "black boxes" with billions of parameters, making observability critical for:

1. Understanding how changes impact outputs: When you modify a prompt or switch models, how does that affect results?
2. Pinpointing and debugging errors: Did your prompt change regress? Identify hallucination, anomalies, security issues or performance bottlenecks.
3. Optimizing for cost and performance: Balance token usage, latency, and output quality.

The Business Benefits of LLM Observability

Beyond just technical understanding, LLM observability also delivers tangible business benefits, such as:

• Reducing operational costs by identifying expensive patterns and optimizing accordingly
• Improving user retention by detecting and fixing poor experiences before they impact users
• Accelerating development cycles by iterating on prompts and debugging faster
• Increasing compliance by maintaining audit trails for regulated industries
• Justifying AI investment by showing clear ROI and performance metrics to stakeholders

As you build your product from prototype to production, monitoring LLM metrics helps you to detect prompt injections, hallucinations and poor user experience, allowing you to improve your prompts for better performance on the go.

LLM Observability vs. Traditional Observability

LLMs are highly complex and contain billions of parameters, making it challenging to understand how prompt changes affect the model's behavior.

While traditional observability like Datadog 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 logging. When you log requests and their corresponding responses, you can easily analyze patterns and understand how context affects the model behavior.

The essential metrics to capture include:

• Input prompts - The exact prompt sent to the model.
• Output completions - The response generated by the model.
• Cost - How much does it cost to generate a response?
• Latency - How long does it take for the model to generate a response?
• Token counts - Prompt tokens, completion tokens, and total tokens.
• Time to First Token (TTFT): How long does it take for the model to generate the first token?
• User identifiers: To track per-user performance and usage patterns
• Session identifiers: For tracking conversation threads
• Custom properties: App-specific metadata for filtering and analysis

Example

// With Helicone proxy integration
const openai = new OpenAI({
  apiKey: process.env.OPENAI_API_KEY,
  baseURL: "https://oai.helicone.ai/v1",
  defaultHeaders: {
    "Helicone-Auth": `Bearer ${process.env.HELICONE_API_KEY}`,
    "Helicone-User-Id": userId,
    "Helicone-Session-Id": sessionId,
    "Helicone-Property-App-Version": "v1.2.3",
    "Helicone-Property-Feature-Flag": "experiment-12"
  }
});

Tracking the conversation in multi-step workflows will help you understand the broader context and pinpoint exactly where it went wrong for debugging purpose.

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, helps you monitor how well the model meets user expectations.

Effective evaluation strategies include:

• Human feedback - Direct user ratings or thumbs up/down on responses
• LLM-as-a-judge - Using another LLM to evaluate outputs
• Ground truth comparison - Automated comparison against known correct answers
• Embedding similarity - Measuring semantic similarity between outputs and references
• Custom heuristics - Domain-specific criteria for output quality

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 focus should be improving its performance.

Key performance metrics include:

• Latency breakdown -
  • End-to-end response time
  • Time to first token (TTFT)
  • Tokens per second
• Error rates by type -
  • API failures
  • Rate limits
  • Context window overflows
• Cache hit rates - For performance optimization
• Cost per request - Token usage translated to actual costs

Tracing your multi-step workflows helps you debug faster and gives you a deeper understanding of your user's journey. Here are some useful examples on debugging agents with Sessions.

4. Anomaly Detection and Feedback Loops

Detecting anomalies, like unusual model behaviors or outputs indicating hallucinations or biases, is essential for maintaining application integrity.

Common anomalies to watch for include:

• Statistical outliers - Responses significantly longer/shorter than normal
• Confidence scores - Unusually low confidence in generated answers
• Semantic drift - Outputs that deviate from expected topics or tone
• Potentially harmful content - Toxic, biased, or unsafe outputs
• Unusual patterns - Sudden changes in user behavior or model performance

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.

Key security considerations include:

• Access control - Limit who can view model inputs/outputs
• Data protection - Safeguard sensitive data and ensure compliance
• Prompt injection protection - Detect and prevent malicious inputs
• Input/output filtering - Screen for PII, toxic content, or confidential information
• Audit trails - Maintain detailed logs for compliance requirements

Protecting sensitive data requires compliance with regulations like GDPR or HIPAA. Maintaining detailed audit trails promotes accountability and aids in meeting compliance requirements. It's all about building user trust!

import OpenAI from "openai";

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

Why Observability is No Longer Optional

The LLM space is evolving so fast. Effective observability was a nice-to-have, but it's becoming essential for any production application. Here's why:

1. LLMs aren't static - Models are continuously updated, and what works today might not work tomorrow
2. Costs add up quickly - Without monitoring, you may be spending far more than necessary
3. User expectations are rising - As LLM applications become commonplace, users expect higher quality and reliability
4. Competitors are watching - Companies with better observability can iterate faster and deliver superior experiences
5. Compliance is coming - Regulations around AI transparency and safety are increasing

Coming Next: Implementation Guide

In our next guide How to Implement LLM Observability for Production (Part 2), we'll dive into:

• Best practices for monitoring LLM performance
• Code examples for implementing each observability pillar
• Step-by-step guide to getting started with Helicone
• Practical next steps for your LLM application

Keep reading to see how we'll turn these concepts into concrete actions.

You might find these useful:

• 5 Powerful Techniques to Slash Your LLM Costs
• Debugging Chatbots and LLM Workflows using Sessions
• How to Test Your LLM Prompts (with Helicone)