LLM Evaluations

In the fast-evolving AI applications, language model evaluations — or “evals” — have become essential for businesses leveraging LLMs in their products. But how can you assess the performance of a language model in a way that not only captures its raw capabilities but also aligns with your unique goals and customer expectations?

Here’s a comprehensive guide to understanding LLM evals and creating a feedback loop that drives meaningful improvement.

What are LLM Evals, and Why Do They Matter?

LLM evaluations measure a model’s effectiveness, consistency, and reliability in a structured way. These metrics aren’t just technical statistics; they reflect how well the model aligns with your goals, from enhancing customer satisfaction to reducing operational costs. Proper evals can give your team clear insights into performance strengths and pinpoint areas needing improvement.

System-Level vs. Task-Level Evals

In LLM evaluations, two main types come into play:

• System-Level Evals: These assessments focus on how the LLM integrates into and impacts your broader system. For example, if your LLM supports customer service, system-level evals will measure its effect on service quality metrics like response time, issue resolution rate, and user satisfaction.
• Task-Level Evals: Task-level evaluations zoom in on the model’s responses to specific prompts or tasks. This granular approach helps you understand how the model performs on individual tasks, such as generating answers to questions or providing recommendations, giving you actionable insights into specific areas of performance.

Designing Custom Evals: Tailoring Metrics to Fit Your Goals

One-size-fits-all metrics don’t account for the unique demands of every product. Custom evals allow you to design metrics that directly align with your objectives and user needs. They’re also easier to interpret and act on, which can be pivotal for getting buy-in from stakeholders. Custom evals connect the dots between evaluation results and business outcomes, demonstrating the direct impact of improvements.

The Importance of Reproducibility

Reproducibility in evals ensures that as you refine your model over time, you’re able to make accurate comparisons across different iterations. A structured evaluation process with well-defined steps and metrics provides consistency, so you can clearly see whether changes made to the model are positively impacting performance.

Building Your Evaluation Metrics

Selecting the right metrics for your evals is a balancing act between quantitative and qualitative measures. Here are some common approaches:

• Statistical Metrics: These measure hard facts, like response accuracy or completion rate.
• Quality-Based Metrics: These address less tangible factors like coherence, tone, and adherence to brand voice.
• Property-Based Unit Tests: Using property-based testing, you can define expected characteristics of a response and automate checks for these properties, ensuring the model meets standards across a variety of inputs.

Together, these metrics can give you a holistic view of model performance, helping you decide where to prioritize adjustments.

Choosing Human vs. LLM Graders

Both human and automated grading (via LLMs) have their strengths:

• Human Graders: Humans are better for subjective aspects like tone, subtlety, and nuanced understanding. Their feedback can provide richer insights but is often costly and time-consuming.
• LLM Graders: Using a model to grade another model’s output allows you to scale evaluations rapidly. This approach is ideal for handling large data volumes where subjective nuance is less critical.

A combined approach often yields the best results—scaling evaluations with LLM graders and using human graders selectively for high-impact or subjective cases.

The Develop-Label-Analyze Loop

LLM evaluations are not one-time tasks but part of a continuous improvement cycle. As you evaluate, you gather data and feedback, which feeds directly into model adjustments. This creates a develop-label-analyze loop where each round of evaluation informs new model adjustments, resulting in a consistently improving LLM. This process is central to keeping your model in line with changing user expectations and emerging business needs.

Closing Thoughts

LLM evals aren’t just technical scorecards—they’re vital tools for enhancing your model’s fit to your specific context. With thoughtful evaluation design, you can create a meaningful feedback loop that drives both your model and business forward. Whether you’re evaluating system-wide impact or fine-tuning specific responses, robust LLM evals provide a clear path to smarter, more effective AI deployment.