Evaluating language models and agents presents a unique challenge in machine learning. Unlike traditional ML systems where metrics like accuracy or F1 score tell a clear story, LLM performance is often subjective and multifaceted. How do you measure the "correctness" of a conversation? What makes one response better than another? These questions lie at the heart of building effective evaluation systems.
The evaluation landscape can be divided into three main categories, each serving different purposes in your development pipeline.
Automated evaluations form the backbone of continuous testing. These are programmatic checks that run without human intervention, measuring specific aspects of your system's performance. Think of them as your first line of defense against regressions and quality issues.
A well-designed automated evaluation suite might check for:
Response quality metrics - measuring factors like coherence, relevance, and toxicity using reference models or specialized classifiers. For example, you might use a smaller LLM to grade the outputs of your production model, checking if responses actually answer the given questions.
Task completion verification - ensuring your agent can successfully complete specific workflows. If your agent is designed to extract information from emails and update a CRM, your eval suite should verify this happens correctly across a variety of email formats and edge cases.
Tool usage accuracy - confirming that agents are using the right tools at the right time and interpreting their outputs correctly. This is particularly crucial for preventing costly mistakes in production.
Perhaps the most critical aspect of evaluation is building high-quality test datasets. This is where theory meets practice, and where many evaluation systems succeed or fail.
Start with your actual use cases. Examine real user interactions, support tickets, or production logs to understand what your system actually needs to handle. Pay special attention to edge cases and failure modes - these often provide the most valuable test cases.
When building your dataset, consider these key principles:
Coverage - Your test cases should span the full range of expected functionality. This means including both common and rare scenarios, different user types, and varying levels of complexity.
Diversity - Include variations in language, tone, and format. If your users might phrase the same request in multiple ways, your test cases should reflect this diversity.
Evolution - Your test suite should grow with your system. When you discover new edge cases or failure modes in production, add them to your evaluation suite to prevent regression.
The most challenging aspect of LLM evaluation is often deciding what to measure. While metrics are essential, choosing the wrong metrics can lead you astray. Consider a customer service agent - measuring just response time might incentivize short, unhelpful answers, while focusing solely on user satisfaction might lead to overly verbose responses that slow down service.