A Unified Evaluation-Instructed Framework for Query-Dependent Prompt Optimization

Researchers introduce a unified evaluation-instructed framework for optimizing AI prompts that adapts to individual queries rather than using static templates. The approach combines a systematic prompt evaluation framework with an execution-free evaluator that predicts quality scores and guides a metric-aware optimizer to rewrite prompts in an interpretable, query-dependent manner, demonstrating consistent improvements across multiple datasets and models.

This research addresses a fundamental limitation in current prompt optimization: the reliance on static templates that fail to adapt to the diverse requirements of different user queries. Traditional methods either refine single templates without contextual awareness or depend on unstable textual feedback and opaque reward models that provide weak optimization signals. The core innovation lies in establishing a performance-oriented evaluation framework that defines prompt quality systematically rather than fragmenting it across unreliable metrics.

The technical contribution centers on developing an execution-free evaluator capable of predicting multi-dimensional quality scores directly from text without requiring actual model execution. This evaluator then instructs a metric-aware optimizer that can diagnose failure modes and rewrite prompts in interpretable ways tailored to specific queries. This represents a significant methodological shift from black-box optimization approaches toward explainable, principled prompt refinement.

The implications extend across AI development and deployment. Organizations building AI systems will benefit from more reliable prompt optimization that adapts to diverse use cases without requiring expensive computational overhead. The model-agnostic nature of the approach means it can work across different language model architectures, increasing its practical utility. The demonstrated improvements across eight datasets and three backbone models suggest the framework captures generalizable principles about prompt effectiveness rather than fitting specific scenarios.

Looking forward, this work opens pathways for automated prompt engineering at scale, potentially reducing the manual effort required to maintain high-performing AI systems. The systematic evaluation framework could become a standard for assessing prompt quality across the industry, moving beyond ad-hoc testing practices currently prevalent in production environments.

• →A unified evaluation framework establishes systematic, performance-oriented metrics for assessing prompt quality across diverse scenarios.
• →An execution-free evaluator predicts multi-dimensional quality scores without requiring actual model runs, reducing computational costs.
• →Query-dependent prompt optimization significantly outperforms static-template baselines across eight datasets and three backbone models.
• →The approach provides interpretable optimization signals through metric-aware diagnosis of failure modes rather than relying on opaque black-box rewards.
• →Model-agnostic design enables the framework to work across different language model architectures without architectural modifications.