Shorter AI instructions performed better in a new prompt-design study
The preprint reports a method that diagnoses mistakes before choosing which instructions to keep.
- Publication
- arXiv
- Stage
- Preprint
- What we read
- Summary of the abstract
What the paper reports
The authors propose ESPO, a process for improving model instructions by grouping errors, trying different revisions and checking which choices remain stable. They compare it with an existing method across seven language-task benchmarks.
Why it matters
The study addresses a practical problem: adding more instructions can make a prompt longer without making answers better.
The reported average accuracy is 74.67%, compared with 70.91% for the comparison method. The resulting prompts were also shorter.
Additional tests used other models. The authors report benefits across those tests while showing that one part of their method could hurt results when used without its selection step.
What this does not tell us
This summary uses the preprint abstract. Benchmark improvements do not guarantee the same gains in a particular workplace or application.
Original sources · 1
- ESPO: Error-Structured Prompt Optimization via Diagnose, Diversify, and Stabilize ↗arXiv · 2026-09-03
Check the original paper for its authors, methods, version and access terms.
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