How to evaluate LLMs
What is LLM Evaluation?
LLM evaluation is the process of assessing the performance, quality, and reliability of large language models across tasks such as text generation, summarization, classification, and multi-turn interactions. Unlike traditional ML, LLM outputs are often non-deterministic, so evaluation must go beyond accuracy or F1 scores.
Why it matters in AI/ML
LLMs are powerful—but unpredictable. A well-evaluated LLM:
- Reduces the risk of hallucination and biased outputs
- Performs consistently across prompt formats or model updates
- Aligns with user expectations in real-world use cases
Without structured evaluation, teams risk shipping unsafe or ineffective AI systems.
How to evaluate LLMs
1. Define evaluation objectives
What are you measuring? Options include:
- Relevance or correctness
- Fluency and grammar
- Factual accuracy
- Safety and appropriateness
2. Use a mix of evaluation methods
- Automated metrics: BLEU, ROUGE, METEOR (limited for generative tasks)
- LLM-as-a-Judge: Have another LLM score outputs based on custom rubrics
- Human review: Annotators score based on clarity, accuracy, or bias
- Behavioral tests: Structured prompt tests across scenarios or edge cases
3. Track prompt and model versions
Keep metadata on which prompt, model, temperature, and provider was used for each run. This helps:
- Identify regressions
- Reproduce strong results
- Compare across models (e.g., GPT-4 vs Claude vs open source)
4. Evaluate across dimensions
A single output may look fine—but evaluation should include:
- Response latency
- Token count and cost
- Failures (e.g., refusals, hallucinations, truncation)
Common challenges
- Outputs are subjective and hard to grade
- Evaluation scales poorly without automation
- Models improve, but prompts don’t—leading to mismatched expectations