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However, naive approaches like full-file, partition-based, or chain-of-thought prompting fail to handle the large, interconnected nature of configuration files. We introduce the Context-Aware Prompting (CAP) framework, which enables an LLM to reason about network configurations like a human expert. CAP first identifies all potentially relevant neighboring, similar, and referenced configuration segments. It then engages the LLM in a structured dialogue, where the model requests the specific context it needs before performing a focused analysis. This on-demand approach provides the necessary context for deep reasoning while avoiding overload. Our evaluation on production network configurations demonstrates that CAP reliably detects a wide range of known and previously unknown configuration errors. CAP substantially outperforms existing LLM-based baselines, while achieving performance comparable to state-of-the-art non-LLM tools. CAP is most effective on misconfigurations that require reasoning over references and intricate dependencies.<\/jats:p>","DOI":"10.1145\/3805631","type":"journal-article","created":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T20:34:18Z","timestamp":1780086858000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["CAP: Detecting Network Device Misconfigurations with Context-Aware Prompting of LLMs"],"prefix":"10.1145","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1652-8419","authenticated-orcid":false,"given":"Xi","family":"Jiang","sequence":"first","affiliation":[{"name":"University of Chicago, Chicago, IL, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3771-7876","authenticated-orcid":false,"given":"Aaron","family":"Gember-Jacobson","sequence":"additional","affiliation":[{"name":"Colgate University, Hamilton, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9315-5201","authenticated-orcid":false,"given":"Nick","family":"Feamster","sequence":"additional","affiliation":[{"name":"University of Chicago, Chicago, IL, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,5,29]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"[n.d.]. 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