Online Prompt Selection revolutionizes program synthesis by automatically selecting the best LLM, prompt style, or symbolic solver for each coding task.

• Addresses the challenge that no single LLM or prompting method works best for all programming tasks
• Dynamically routes synthesis problems to the most effective solving approach based on task characteristics
• Achieves 17.3% higher success rates compared to any single prompting strategy
• Reduces computation costs by avoiding unnecessary model calls

This research enables more reliable automated programming tools that can adaptively leverage the strengths of different models and solution approaches, dramatically improving efficiency in software development pipelines.

Original Paper: Online Prompt Selection for Program Synthesis