PRISM: Prompt Reliability via Iterative Simulation and Monitoring for Enterprise Conversational AI
The article introduces PRISM, a framework designed to enhance the reliability of prompts used in enterprise conversational AI. It emphasizes the need for continuous monitoring and optimization of prompts to address behavioral drift in large language models. The framework significantly reduces prompt authoring time and achieves high production reliability across various agents.
- ▪PRISM treats prompt engineering as a continuous reliability engineering problem rather than a one-time task.
- ▪The framework reduces median prompt authoring time from 2 days to under 30 minutes.
- ▪PRISM achieves 99% production reliability and identifies prompt regressions within a 24-hour detection window.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.15665 |
| Publication time | Mon, 18 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-18T04:04:54.418Z |
| Last seen | 2026-05-18T04:04:54.418Z |
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| Summary source text | contentText |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Computer Science > Artificial Intelligence arXiv:2605.15665 (cs) [Submitted on 15 May 2026] Title:PRISM: Prompt Reliability via Iterative Simulation and Monitoring for Enterprise Conversational AI Authors:Keshava Chaitanya, Jahnavi Gundakaram View a PDF of the paper titled PRISM: Prompt Reliability via Iterative Simulation and Monitoring for Enterprise Conversational AI, by Keshava Chaitanya and 1 other authors View PDF HTML (experimental) Abstract:Deploying large language model (LLM)-driven conversational agents in enterprise settings requires prompts that are simultaneously correct at launch and resilient to the non-deterministic behavioral drift that characterizes production LLM deployments.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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