Rules or Examples in LLM Context: What Works Best?
A study exploring the impact of rules vs examples in LLM contexts, focusing on data analytics use cases.
A study was conducted to evaluate why examples are often recommended for improving LLM performance. It found that shorter derivation distances, achieved through comprehensive example libraries, significantly enhance accuracy in challenging queries. The findings suggest that smaller models benefit from detailed examples, while stronger models perform well with leaner contexts.
This synthesis was produced from its source by AI; there is no human editor or manual review step. How we work