Researchers explored ways to improve the accuracy of Large Language Model (LLM) intent classification prompts. They experimented with various modifications to descriptions, including adding prefixes, suffixes, and capitalization, as well as using AI-generated descriptions. The results showed that these modifications can lead to small but measurable improvements in accuracy, particularly when combined with other techniques such as structuring formatting. The top-performing configurations involved adding prefixes, using AI-generated descriptions, and paying attention to edge cases. While the gains were not dramatic, the study suggests that optimizing prompt design is an important area of research for improving LLM performance.