llm-evaluation

Benchmark LLM outputs by providing test inputs and model responses to calculate automated metrics like BLEU, ROUGE, METEOR, BERTScore, and perplexity, which quantifies quality and detects regressions across different prompts.

Compare LLM models with A/B testing by running structured evaluations side-by-side using automated metrics and human judgments. This quantifies improvements, validates model upgrades, and establishes reproducible baselines for production readiness.

Yes, you can use human evaluation for LLM accuracy and safety. The framework supports evaluating outputs across specific dimensions including accuracy, coherence, relevance, safety, and helpfulness to measure AI quality and guide improvements.

The LLM-as-Judge pattern works by using a language model to evaluate outputs within structured frameworks. It enables robust comparisons and scalable evaluation across prompts, models, and tasks to quantify quality without manual review.

You need custom metrics for NLP evaluation workflows when standard automated metrics are insufficient. The framework supports custom metrics and reproducible workflows with a scalable harness to establish baselines, compare improvements, and detect regressions.

Automated metrics for LLM evaluation have limitations in assessing subjective dimensions like safety and helpfulness. Combining them with human evaluation and LLM-as-Judge patterns ensures robust comparisons and accurate quality measurement across tasks.