Unexpected progress in smart programs now shapes daily work in writing, programming, code fixes. As big language tools appear, old ways shift fast - yet better results do not come automatically. Often, flaws stem less from tech limits than weak human requests. Unclear questions tend to produce mismatched answers, raising workload while slowing output. While some sites allow sharing those queries, few offer solid ways to judge their strength, leaving users sifting through clutter where most entries add little value. Facing these issues, PromptX applies a structured method meant to improve how prompts work in practice. Through automatic assessments paired with insights from users, it judges what truly performs well. Instead of favoring only widely used options, relevance within context takes priority. As selections adapt gradually, less useful repetitions fade out naturally. Suggestions shift quietly based on individual habits and changing needs. Performance stays stable whether crafting text, solving problems, or generating code. With repeated use, refinements emerge without interruption. The system evolves simply because it is used
Shevatekar et al. (Thu,) studied this question.