How to Develop Powerful Internal LLM Benchmarks

Public benchmarks for large language models (LLMs) are often flawed because model developers are incentivized to optimize performance specifically for them. A more effective evaluation method is to create custom, internal benchmarks tailored to specific use cases, which should be automated and produce a numeric score for comparison. These benchmarks should ideally use internal data not available online to avoid contamination from the models' training data. Regularly testing both proprietary models like GPT and Gemini, as well as open-source alternatives, on these custom tasks ensures the selection of the best model for a specific application.