Download PDFOpen PDF in browserA Vision for Sustainable AI-Assisted Development: Semantic Retrieval for Efficient AI Code Generation4 pages•Published: April 19, 2026AbstractRecent advancements in Artificial Intelligence (AI) code generation have given rise to a practice called vibe-coding where a code-generation tool writes the code while the developer primarily provides feedback and corrections. Tools like Cursor and GitHub Copilot have popularized it by directly integrating AI code generation into the IDE, thus making it easier to use. However, users of these tools often err on the side of providing excessive content; often providing whole dependency trees and code bases despite the decreasing accuracy and increasing energy consumption. In the paper that follows, we outline a vision for a system that extracts minimal, but sufficient context for these code generation systems. We argue that current context management systems are a critical bottleneck and propose a research agenda based on semantic retrieval to address this issue. We explore open questions and future directions that could make AI-assisted development more efficient and cost-effective.Keyphrases: ai assisted programming, context optimization, large language models, semantic code retrieval, sustainable software development In: Jernej Masnec, Hamid Reza Karimian, Parisa Kordjamshidi and Yan Li (editors). Proceedings of AI for Accelerated Research Symposium, vol 3, pages 89-92.
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