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Navigating LLMs in Software Development
This chapter examines the strengths and weaknesses of large language models (LLMs) in software development tasks, particularly when interfacing with existing codebases. It underscores the complexity of generating patches and emphasizes the need for improved feedback mechanisms and better contextual inputs to enhance LLM performance. The discussion also previews future advancements in AI-driven software development, highlighting the necessity for new abstractions to bridge the gap between AI capabilities and developer expectations.