LLM evaluation gaps: Changes outside prompts cause regressions

A common issue in evaluating Large Language Models (LLMs) is that automated testing often fails to catch regressions caused by changes outside of prompt files. These regressions can stem from various factors such as tokenizer updates, changes in retrieval indexes, modifications to tool schemas, or shifts in model provider aliases. Current continuous integration (CI) systems typically only trigger evaluations when prompt files are altered, leaving a significant gap in coverage. To address this, developers should expand their evaluation triggers to encompass all inputs that can affect model output, including tokenizers, decoding parameters, and embedding models, ensuring that any change is properly tested. AI

IMPACT Highlights critical gaps in LLM evaluation, urging developers to broaden testing beyond prompt changes to ensure model stability.

RANK_REASON The item discusses best practices and potential pitfalls in LLM evaluation, offering commentary on existing systems rather than announcing a new release or research finding.

Read on dev.to — LLM tag →

• Anthropic
• continuous integration
• GPT-4o
• retrieval-augmented generation

• infra
• product

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