Why You Need Continuous LLM Evaluation (And Why It's Been So Hard)
Jan 1, 2025
I recently built something I wish I'd had at my last startup. We were building on top of an LLM, and it felt like trying to nail jello to a wall. Fix one issue, cause three more. The worst part? We couldn't even measure how bad the regressions were.
This is the dirty secret of LLM-powered apps. Everyone's building them, but no one seems to have cracked the code on monitoring them effectively at scale. The root of the issue is simple but profound: LLMs are non-deterministic. They're incredibly powerful, but also unpredictable. Your app might work perfectly one day and spout nonsense the next.
For pre-launch startups without large datasets, this problem is even more acute. How do you evaluate performance when you don't have real-world data to test against?
Existing solutions didn't cut it. They were either far too expensive for a startup budget or so generic as to be almost useless for specific use cases. I knew there had to be a better way.
A New Approach to LLM Evaluation
So I built a system for continuous LLM evaluation. The core idea is to create a custom evaluation framework for each unique use case, then monitor performance consistently over time. Let me break down the two key components:
1. Custom Domain Breakdowns
Take a look at this sample radar chart:
XThis chart compares different language models across five key dimensions: Composition, Creativity, Consistency, Vocabulary, and Humor. What's crucial here isn't the specific dimensions, but the idea that different applications need different evaluation criteria.
If you're building a creative writing assistant, you might care deeply about Creativity and Vocabulary. For a technical documentation tool, Consistency and Composition might be your north stars. The point is, you need to identify the metrics that matter for your specific use case.
In my system, we work with developers to define these custom metrics. It's not a one-size-fits-all approach, but a tailored evaluation framework for each unique application.
2. Continuous Monitoring and Regression Alerting
Now, look at this (simplified) line graph:
This graph shows "Factual Recall" performance over nine different builds of an LLM application. Notice how performance remains fairly stable for the first eight builds, then drops dramatically in build nine.
This graph illustrates why continuous monitoring is so critical. Without it, you might ship build nine without realizing you've introduced a major regression in factual recall. But it's not just about catching regressions. Continuous monitoring helps you:
- Identify performance improvements
- Track performance drift over time
- Understand the impact of data updates or model changes
The Process in Action
Here's how the system works in practice:
1. We start by deeply understanding your use case and prompt structure.
2. Together, we define custom metrics that truly matter for your application.
3. We help generate a representative dataset, either from your existing data or through synthetic generation.
4. We create a "golden set" of model completions, verified by humans for quality.
5. We generate preference data to train a reward model.
6. We set up a continuous integration pipeline to track performance over time.
The result? You're not flying blind anymore. You have concrete data on how your LLM application is performing across the metrics that actually matter to your users.
Reflections on LLM Development
Building with LLMs still feels a bit like black magic sometimes. But with a robust evaluation system in place, at least you know when your spell has gone awry - and you have the tools to correct course.
I built this system out of frustration with my own experiences, but I'm excited to see how it might help other developers. As LLMs become more ubiquitous in software development, having strong evaluation frameworks will be crucial.
For those building LLM applications, I'd love to hear about your experiences. What challenges have you faced in evaluating and monitoring your models? How have you addressed them? Let's push this field forward together.
I recently built something I wish I'd had at my last startup. We were building on top of an LLM, and it felt like trying to nail jello to a wall. Fix one issue, cause three more. The worst part? We couldn't even measure how bad the regressions were.
This is the dirty secret of LLM-powered apps. Everyone's building them, but no one seems to have cracked the code on monitoring them effectively at scale. The root of the issue is simple but profound: LLMs are non-deterministic. They're incredibly powerful, but also unpredictable. Your app might work perfectly one day and spout nonsense the next.
For pre-launch startups without large datasets, this problem is even more acute. How do you evaluate performance when you don't have real-world data to test against?
Existing solutions didn't cut it. They were either far too expensive for a startup budget or so generic as to be almost useless for specific use cases. I knew there had to be a better way.
A New Approach to LLM Evaluation
So I built a system for continuous LLM evaluation. The core idea is to create a custom evaluation framework for each unique use case, then monitor performance consistently over time. Let me break down the two key components:
1. Custom Domain Breakdowns
Take a look at this sample radar chart:
XThis chart compares different language models across five key dimensions: Composition, Creativity, Consistency, Vocabulary, and Humor. What's crucial here isn't the specific dimensions, but the idea that different applications need different evaluation criteria.
If you're building a creative writing assistant, you might care deeply about Creativity and Vocabulary. For a technical documentation tool, Consistency and Composition might be your north stars. The point is, you need to identify the metrics that matter for your specific use case.
In my system, we work with developers to define these custom metrics. It's not a one-size-fits-all approach, but a tailored evaluation framework for each unique application.
2. Continuous Monitoring and Regression Alerting
Now, look at this (simplified) line graph:
This graph shows "Factual Recall" performance over nine different builds of an LLM application. Notice how performance remains fairly stable for the first eight builds, then drops dramatically in build nine.
This graph illustrates why continuous monitoring is so critical. Without it, you might ship build nine without realizing you've introduced a major regression in factual recall. But it's not just about catching regressions. Continuous monitoring helps you:
- Identify performance improvements
- Track performance drift over time
- Understand the impact of data updates or model changes
The Process in Action
Here's how the system works in practice:
1. We start by deeply understanding your use case and prompt structure.
2. Together, we define custom metrics that truly matter for your application.
3. We help generate a representative dataset, either from your existing data or through synthetic generation.
4. We create a "golden set" of model completions, verified by humans for quality.
5. We generate preference data to train a reward model.
6. We set up a continuous integration pipeline to track performance over time.
The result? You're not flying blind anymore. You have concrete data on how your LLM application is performing across the metrics that actually matter to your users.
Reflections on LLM Development
Building with LLMs still feels a bit like black magic sometimes. But with a robust evaluation system in place, at least you know when your spell has gone awry - and you have the tools to correct course.
I built this system out of frustration with my own experiences, but I'm excited to see how it might help other developers. As LLMs become more ubiquitous in software development, having strong evaluation frameworks will be crucial.
For those building LLM applications, I'd love to hear about your experiences. What challenges have you faced in evaluating and monitoring your models? How have you addressed them? Let's push this field forward together.