LLM Observability: 5 Essential Pillars for Production-Ready AI Applications

Lina Lam· April 12, 2025

Building a reliable LLM application in production is incredibly challenging. When companies move their LLM applications from proof-of-concept to production, many are discovering the harsh reality that traditional monitoring tools simply fall short.

In this guide, we'll explore key pillars of LLM observability, and how you can maintain the reliability of your LLM applications in production.

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 LLM tracing, to evaluating the LLM outputs.

As your product transitions from prototype to production, monitoring becomes crucial in detecting hallucinations, debugging complex agentic workflows, and fine-tuning your model for better performance on the go.

Traditional vs. LLM Observability: What's the Difference?

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 key 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.

Why Your LLM Application Needs Specialized Observability

Hallucinations, spiraling costs, and user complaints about response times cannot be solved with traditional monitoring tools because LLM applications are fundamentally more complex than conventional software.

Simply put:

The Five Pillars of LLM Observability

1. Traces & Spans

At the core of LLM observability is the detailed logging and tracing of workflows through your application. This includes tracking both requests and responses, as well as the multi-step interactions that often characterize LLM applications.

Comprehensive traces capture the journey of a user interaction from initial prompt to final response, helping you understand complex conversations and context over time. This is especially valuable for debugging and optimizing multi-step workflows.

• Request and Response Logging: Capturing raw requests to LLM services and their corresponding responses, along with metadata like latency, token counts, costs, or custom ones.
• Multi-Step Workflow Tracing: Using tools like Helicone's Sessions to track user journeys across multiple interactions, making it easier to debug complex agent-based systems.
• Anomaly Detection: Identifying patterns of unusual behavior, potential failures, or hallucinations by analyzing traces across multiple user sessions.

Here's a screenshot of the dashboard that shows aggregated metrics for traces:

Monitoring tools like Helicone typically capture other useful metrics like latency, costs, Time to First Token (TTFT), and more. All these traces create a comprehensive view of your application's behavior over time.

2. LLM Evaluation

Assessing the quality of your model's outputs is vital for continuous improvement. This pillar focuses on measuring how well your LLM performs against specific criteria and expectations.

Some effective evaluation practices include:

• Online and Offline Evaluation: Testing model outputs both in real-time (online) and through batch processing of historical data (offline).
• User Feedback: Gathering direct input from users to understand whether model responses meet their expectations.
• Automated Evaluation: Using LLM-as-judge or other programmatic methods to consistently assess outputs when human evaluation isn't practical.
• Regression Prevention: Identifying when changes to prompts or models result in decreased performance.

These evaluation practices help you continuously improve accuracy and reduce unwanted behaviors like hallucinations. There are existing tools that can help you set up these practices.

3. Prompt Engineering

Writing effective prompts is both art and science. This pillar focuses on systematically testing, refining, and managing the inputs you provide to LLMs.

Prompt engineering is one of the most important aspects of LLM observability. Good prompt engineering practices lead to more reliable outputs, better user experiences, and often reduced costs through more efficient token usage.

There are prompt management tools out there like Helicone that help you with:

• Prompt versioning: Keeping track of prompt changes and roll back any time in case of prompt regression.
• A/B testing: Comparing different versions of prompts to push the best performing one to production.
• Prompt templates: Standardizing successful prompt patterns for any user input.
• Hallucination reduction: Refining prompts to minimize incorrect or fabricated information.
• Experiment with production data: Systematically test your prompts on production data before shipping them to production.

4. Search and Retrieval

For knowledge-intensive applications, the quality of information provided to the LLM is crucial. This pillar focuses on optimizing how relevant content is retrieved and incorporated into the generation process.

Here are some ways you can improve the accuracy of your LLM outputs:

• Using Retrieval Augmented Generation (RAG): Integrating relevant external information to LLM outputs.
• Using Tool Calls: Tool calls allows you to integrate specialized functions for LLMs to perform specific tasks.
• Use Vector Database: Optimizing how information is stored and retrieved for use in prompts.
• Create Quality Metrics: Measuring how well your system fetches relevant information to support LLM responses.

Effective search and retrieval mechanisms help ground LLM responses in accurate information, reducing hallucinations and improving factuality. It's also why Helicone's Sessions is one of the most used features. Developers use it to trace the entire LLM workflow that may contain tool calls, RAG, and more.

5. LLM Security

Ensuring the safety and integrity of your LLM application is non-negotiable. This pillar addresses potential vulnerabilities and abuse scenarios unique to language models.

a) LLM-Specific Protections

Production applications require robust security measures to build trust with your users and prevent potential misuse. Implementing safeguards against prompt injections and other LLM-specific cybersecurity attacks is crucial.

b) Custom Rate Limits

To further protect your application from abuse or unexpected costs, you can set custom rate limits to control LLM usage by users.

In Helicone, there's a simple way to do this by adding the following header to your requests:

"Helicone-RateLimit-Policy": "100;w=3600;u=request;s=user"

This would limit each user to 100 requests per hour. You can also limit by cost using cents as the unit, helping you maintain predictable spending even as your application scales.

Coming Next: Implementation

In our next guide How to Implement LLM Observability for Production, we'll dive into best practices for monitoring LLM performance, code examples for implementing each observability pillar, and a step-by-step guide to getting started with Helicone.

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

We are here to help you every step of the way! If you have any questions, please reach out to us via email at [email protected] or through the chat feature in our platform. Happy monitoring!

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)