Prompt Repeat - a simple option with measurable impact

We’ve just added a Prompt Repeat option to Zeaware Avalon, allowing instructions to be selectively repeated during prompt assembly. In the right situations, this can help improve accuracy, consistency, and predictability, without changing models, pipelines, or architectures.

Where Prompt Repeat helps most

• Classification
• Structured / fixed-schema extraction
• Intent detection
• Rule-constrained responses
• Validation and completeness checks

This feature is informed by recent research, including “Prompt Repetition Improves Non-Reasoning LLMs” (arXiv:2512.14982), which reported 3–10% absolute accuracy improvements on non-reasoning tasks simply by repeating the user query (e.g. Q → Q Q). Gains were strongest where instruction adherence matters more than step-by-step reasoning, and were achieved without modifying the underlying model.

Designed for controlled use

In Zeaware Avalon, Prompt Repeat is configurable (applied only where it helps), scoped (instructions, not entire contexts), and governed (designed to preserve deterministic outputs).

It’s a useful reminder that improving AI systems isn’t always about bigger models or more complexity, sometimes it’s about execution of simple, evidence-backed ideas.

Note: Prompt Repeat increases input token usage and may result in higher token consumption. As with all prompt-level techniques, observed improvements can vary by model, task, and data characteristics. Prompt Repeat is therefore configurable and should be applied selectively where it provides measurable benefit.