evaluation-methodology

You can evaluate LLM outputs using exact match, semantic similarity, LLM-as-judge, or ELO ranking. These methods allow you to objectively compare response accuracy and helpfulness across different model versions.

Semantic similarity calculates how close two model responses are in meaning, while ELO ranking provides comparative evaluation by updating model scores based on head-to-head performance. Both methods combine to measure AI quality precisely.

Yes, this Skill uses sentence-transformers for semantic similarity calculation and scikit-learn for quality metrics. These dependencies are required to run the provided Python scripts for evaluating and comparing model performance.

Comparative evaluation with ELO rating works by updating model scores after direct comparisons of responses. It ranks different AI models against each other based on their performance in answering benchmark queries.

Yes, you can use this Skill to build a structured evaluation pipeline for chatbots. It provides a clear framework for comparing the helpfulness and accuracy of responses from different LLM versions against benchmark queries.

Use exact match for straightforward factual accuracy, and use LLM-as-judge for evaluating complex response quality like helpfulness. Semantic similarity and ELO ranking offer additional comparative evaluation methods for broader domains.