{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T08:25:51Z","timestamp":1786955151717,"version":"build-2736575974"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032013767","type":"print"},{"value":"9783032013774","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Large Language Models (LLMs) have become valuable tools for developers with their ability to accelerate software development. However, LLMs are usually trained on standard programming languages like C\/C++, Java, Python, and JavaScript. There has been little research in the usage of LLMs as development tools for less commonly used languages especially when it comes to domain-specific languages like Lingua Franca. This project shows how to curate context to guide the LLM. We demonstrate the technique by showing how to generate code that uses the Lingua Franca coordination language with Python.<\/jats:p>","DOI":"10.1007\/978-3-032-01377-4_2","type":"book-chapter","created":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T22:23:13Z","timestamp":1759270993000},"page":"30-47","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Context Engineering for\u00a0AI-Assisted Programming for\u00a0Domain-Specific Languages"],"prefix":"10.1007","author":[{"given":"Moez","family":"Ben Hajhmida","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Edward A.","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,10,1]]},"reference":[{"key":"2_CR1","unstructured":"Brown, T., et al.: Language models are few-shot learners. 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Findings of the Association for Computational Linguistics: EMNLP 2021, pp. 2719\u20132734. Association for Computational Linguistics, Punta Cana, Dominican Republic (November 2021)","DOI":"10.18653\/v1\/2021.findings-emnlp.232"},{"key":"2_CR13","unstructured":"Rozi\u00e8re, B., et al.: Code Llama: Open Foundation Models for Code. ArXiv abs\/2308.12950 (2023)"},{"key":"2_CR14","unstructured":"Shao, Z., Dai, D., Guo, D., Liu), B.L.B., Wang, Z., Xin, H.: DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model. ArXiv abs\/2405.04434 (2024)"},{"key":"2_CR15","doi-asserted-by":"crossref","unstructured":"Thakur, S., et al.: Benchmarking large language models for automated verilog RTL Code generation. 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