What is LLM Evaluation? Testing AI Quality

LLM evaluation is the process of measuring how well a language model performs on specific tasks. Unlike traditional software testing, LLM evaluation must account for the probabilistic nature of AI outputs.

Why LLM Evaluation Matters

Without proper evaluation, you can't know if your AI is:

Producing accurate, helpful responses
Avoiding harmful or biased outputs
Improving or degrading over time
Meeting business requirements

Evaluation Methods

Automated Metrics

BLEU/ROUGE: Text similarity to reference outputs
Perplexity: Model confidence in predictions
Semantic similarity: Embedding-based comparison
Task-specific: Accuracy, F1, exact match

LLM-as-Judge

Using another LLM to evaluate outputs. Effective for subjective qualities like helpfulness, but can inherit biases.

Human Evaluation

Gold standard for quality assessment. Essential for safety-critical applications but expensive and slow.

Key Metrics to Track

Accuracy: Correctness of factual claims
Relevance: Response addresses the query
Coherence: Logical, well-structured output
Safety: Absence of harmful content
Groundedness: Claims supported by sources

Production Evaluation

Evaluation doesn't stop at deployment. Production systems need continuous monitoring:

Track quality metrics over time
Detect model drift and degradation
Sample outputs for human review
Monitor safety classifications

DriftRail for Evaluation

DriftRail provides automated evaluation through its detection types:

Hallucination detection for accuracy
Confidence analysis for certainty
Toxicity detection for safety
Industry benchmarks for comparison

FAQ

How often should I evaluate my LLM?

Continuously in production. Run formal evaluations before deployments and after model updates. Monitor key metrics daily.

What's a good accuracy rate for LLMs?

Depends on the use case. Customer support might accept 90%, while medical applications need 99%+. Define acceptable thresholds based on risk.