Prompt Picker: Find the Best LLM System Prompts

Choosing the right system prompt for an LLM-powered application often comes down to guesswork, but Prompt Picker replaces that guesswork with data. The tool lets you run structured experiments across multiple candidate system prompts, simulated user inputs, and model parameters, then evaluates the resulting generations in a double-blind format so your judgments stay unbiased. By comparing outputs head-to-head and using an ELO-based ranking system, Prompt Picker surfaces which exact prompt configuration performs best for your use case. This makes it especially valuable for teams building chat-based products, custom instruction sets, or any application where the quality of the system prompt directly impacts user experience and operational costs. Instead of manually eyeballing outputs or relying on intuition, developers and prompt engineers can quantify prompt performance, shorten prompts to cut token costs, and confidently select the configuration that produces the most helpful, consistent responses.

1. Configure your experiment by defining the system prompts you want to test, sample user inputs, and the language model parameters to use. 2. Assess the resulting generations through a double-blind comparison process to avoid bias in your judgments. 3. Review the results as Prompt Picker ranks each prompt, helping you identify and adopt the best-performing configuration.