arXiv:2606.11459v1 Announce Type: new Abstract: Large Language Models are highly sensitive to prompt formulation, necessitating automatic prompt optimization to unlock their full potential. While evolutionary algorithms have emerged as the dominant paradigm, they suffer from a critical bottleneck: data efficiency. Current methods treat the development dataset as a static benchmark, wasting significant compute budget on uninformative data. In this work, we introduce APEX (Automatic Prompt Engineering eXpert), a novel framework that optimizes the data usage alongside the prompt search. APEX dyna
The rapid development and widespread adoption of Large Language Models necessitate more efficient and automated prompt engineering techniques to unlock their full potential and address current scaling limitations.
Improving prompt engineering efficiency can significantly reduce compute costs and development cycles for AI applications, democratizing access to performant LLMs and accelerating innovation.
The introduction of frameworks like APEX will shift prompt engineering from a labor-intensive, iterative process into a more automated and data-efficient optimization task, making LLMs more accessible and cost-effective.
- · AI developers
- · Cloud providers
- · SaaS companies leveraging LLMs
- · Generative AI platforms
- · Manual prompt engineers
- · Companies with inefficient LLM deployment strategies
Automated prompt optimization tools will become standard in LLM development pipelines.
Reduced compute costs will enable more complex or niche LLM applications to become economically viable.
The development of more sophisticated AI agents could accelerate as their underlying LLM interactions become more efficiently optimized.
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Read at arXiv cs.CL