Bay Information Systems

LLM Evaluation Metrics

Evaluating large language models remains one of the more challenging aspects of deploying AI systems in production. Unlike traditional software, where outputs are deterministic and testable through unit tests, LLM outputs are probabilistic and context-dependent. This creates a need for structured evaluation approaches that can quantify model performance across different dimensions.

This article provides a reference framework for selecting and implementing evaluation metrics, with particular focus on fine-tuning workflows and retrieval-augmented generation (RAG) systems. The structure and categorisation presented here draws from the deepeval library and its associated documentation, which provides practical implementations of these evaluation approaches.

Why Metrics Matter

Metrics serve three primary functions in LLM development:

Without appropriate metrics, teams risk deploying systems that appear functional in development but fail under production conditions.

Core Evaluation Dimensions

Effective evaluation typically requires metrics across multiple dimensions. The specific combination depends on the use case and system architecture, but most production systems benefit from coverage in these areas:

RAG-Specific Metrics

RAG systems introduce additional evaluation requirements beyond standard LLM metrics, as they involve both retrieval and generation stages.

Relevancy measures whether the retrieval component surfaces documents that contain information relevant to the query. This can be assessed through:

Utilisation measures how effectively the LLM uses the provided context:

End-to-End requires measuring the combined performance of retrieval and generation:

Fine-Tuning Evaluation

Fine-tuning workflows require metrics that can compare model versions and track improvement over iterations.

During fine-tuning, standard training metrics provide initial feedback:

These metrics are necessary but insufficient, as they do not directly measure task performance.

Fine-tuned models should be evaluated on the specific task they were trained for:

When fine-tuning for one capability, it is important to avoid intoducing regressions:

Responsible AI Metrics

Production systems require evaluation for potential harms:

Bias metrics assess whether the model produces systematically different outputs based on demographic or other sensitive attributes. This can be measured through:

Toxicity metrics detect harmful, offensive, or inappropriate content. Common approaches include:

Implementation Considerations

Most production systems benefit from 3-5 core metrics rather than comprehensive coverage of all possible dimensions. The selection should be driven by:

Overly broad metric coverage adds complexity without necessarily improving insight.

Modern LLM evaluation increasingly relies on LLM-as-judge approaches, where a separate (often more capable) LLM evaluates outputs. This is more flexible than traditional statistical methods (BLEU, ROUGE) but introduces considerations:

Statistical methods remain useful for specific cases (exact match for classification, edit distance for correction tasks) but generally lack the semantic understanding required for open-ended text evaluation.

Metrics are most useful when compared against baselines, established from:

Practical Implementation

Modern evaluation frameworks provide pre-built metrics alongside the flexibility to define custom ones. The deepeval library (https://github.com/confident-ai/deepeval) offers implementations of many metrics discussed in this article, including correctness, hallucination, contextual relevancy, and bias detection. Such frameworks reduce implementation overhead while maintaining the flexibility to define task-specific evaluation criteria.

When implementing evaluation:

Summary

Effective LLM evaluation requires selecting metrics that align with use case requirements and system architecture. For RAG systems, this means measuring both retrieval quality and generation quality. For fine-tuning, this means tracking task performance while monitoring for regression in other capabilities.

The specific metrics chosen matter less than the discipline of quantifying performance, establishing baselines, and using results to guide development decisions. Production-ready systems should include automated evaluation, clear performance thresholds, and ongoing monitoring.

Quick Reference: Common Metrics by Use Case

RAG Systems

Fine-Tuned Models

General LLM Applications

Conversational Systems

This reference should provide a starting point for teams implementing evaluation pipelines. The specific implementation details will vary based on available tools and infrastructure, but the fundamental evaluation dimensions remain consistent across most production LLM applications.

References

This article draws from the evaluation framework and documentation provided by the deepeval project: