arXiv:2606.30840v1 Announce Type: new Abstract: LLM agents are becoming central to information retrieval: they issue retrieval queries, synthesize answers, and increasingly serve as judges for IR evaluation. Improving the prompts that control these agents is an optimization problem, but in applied IR settings it often looks less like blind search and more like debugging. Engineers need to know which behavior failed, which nearby behavior still worked, what distinguishes the two, and whether a prompt edit improves held-out quality without introducing regressions. We present Contrastive Reflecti
The rapid advancement and integration of LLM agents in critical workflows necessitates more sophisticated and efficient prompt optimization techniques, moving beyond manual trial-and-error.
Improving prompt optimization directly enhances the reliability, efficiency, and debugging capabilities of LLM agents, which are becoming central to complex information retrieval and decision-making systems.
Prompt engineering shifts from a 'blind search' to a more structured, debug-oriented process, allowing engineers to diagnose and mitigate specific agent failures with greater precision.
- · LLM application developers
- · prompt engineers
- · AI-driven information retrieval platforms
- · enterprises adopting LLM agents
- · manual prompt tuning methods
- · inefficient AI agent deployments
Increased reliability and performance of LLM agents in information retrieval and synthesis tasks.
Faster development cycles and lower operational costs for AI agent-based products and services.
Enhanced trust and broader adoption of autonomous AI agents across various industries, accelerating workflow automation.
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Read at arXiv cs.AI