Best Practices for Evaluating Large Language Model Applications with llmeval: Niklas Nielsen
Feb 5, 2025 · 9:33
Niklas Nielsen, CTO and co-founder of Log10, introduces llmeval, a command-line tool that enables teams to ship reliable LLM applications by evaluating and testing prompts and configurations. The tool initializes with four lines of code, uses Meta's Hydra for configurable test structures, and runs multiple samples (default 5) per test to assess stability. Nielsen demonstrates prompt engineering for a math problem, showing how stripping spaces or adding instructions like 'only return the answer' affects strict pass/fail results across Claude, GPT-4, and GPT-3.5. Advanced use cases include testing tools for Python code generation and model-based evaluation, where a larger model grades outputs on criteria like mermaid diagram quality. He notes pitfalls: models favor their own output and struggle with point scores. Log10 addresses this by bridging model-based and human feedback—collecting prior human ratings to train an 'auto-John' that pre-fills reviews for new completions.
