# Prompt Smart, Pay Less: Cost-Aware APO for Real-World Applications

arXiv: https://arxiv.org/abs/2507.15884 · HTML: https://mlsanity.com/paper/2507.15884/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 16 references that are in the map.
- First posted on arXiv 18 Jul 2025.

## Builds on (most cited first, 9 of 9)

- [Automatic Prompt Optimization with "Gradient Descent" and Beam Search](https://mlsanity.com/paper/2305.03495/) (2023)
- [Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution](https://mlsanity.com/paper/2309.16797/) (2023)
- [PromptAgent: Strategic Planning with Language Models Enables Expert-level Prompt Optimization](https://mlsanity.com/paper/2310.16427/) (2023)
- [Unleashing the Potential of Large Language Models as Prompt Optimizers: Analogical Analysis with Gradient-based Model Optimizers](https://mlsanity.com/paper/2402.17564/) (2024)
- [SPRIG: Improving Large Language Model Performance by System Prompt Optimization](https://mlsanity.com/paper/2410.14826/) (2024)
- [CriSPO: Multi-Aspect Critique-Suggestion-guided Automatic Prompt Optimization for Text Generation](https://mlsanity.com/paper/2410.02748/) (2024)
- [GReaTer: Gradients over Reasoning Makes Smaller Language Models Strong Prompt Optimizers](https://mlsanity.com/paper/2412.09722/) (2024)
- [SCULPT: Systematic Tuning of Long Prompts](https://mlsanity.com/paper/2410.20788/) (2024)
- [Dual-Phase Accelerated Prompt Optimization](https://mlsanity.com/paper/2406.13443/) (2024)

## Built on by (most cited first, 2 of 2)

- [PrismVAU: Prompt-Refined Inference System for Multimodal Video Anomaly Understanding](https://mlsanity.com/paper/2601.02927/) (2026)
- [The Biosecurity Blind Spot: Systematic Dual-use Detection in Open Science Infrastructure](https://mlsanity.com/paper/2605.28843/) (2026)

## Abstract (arXiv)

Prompt design is a critical factor in the effectiveness of Large Language Models (LLMs), yet remains largely heuristic, manual, and difficult to scale. This paper presents the first comprehensive evaluation of Automatic Prompt Optimization (APO) methods for real-world, high-stakes multiclass classification in a commercial setting, addressing a critical gap in the existing literature where most of the APO frameworks have been validated only on benchmark classification tasks of limited complexity. We introduce APE-OPRO, a novel hybrid framework that combines the complementary strengths of APE and OPRO, achieving notably better cost-efficiency, around 18% improvement over OPRO, without sacrificing performance. We benchmark APE-OPRO alongside both gradient-free (APE, OPRO) and gradient-based (ProTeGi) methods on a dataset of ~2,500 labeled products. Our results highlight key trade-offs: ProTeGi offers the strongest absolute performance at lower API cost but higher computational time as noted in~citeprotegi, while APE-OPRO strikes a compelling balance between performance, API efficiency, and scalability. We further conduct ablation studies on depth and breadth hyperparameters, and reveal notable sensitivity to label formatting, indicating implicit sensitivity in LLM behavior. These findings provide actionable insights for implementing APO in commercial applications and establish a foundation for future research in multi-label, vision, and multimodal prompt optimization scenarios.