Large language models significantly impact programming by changing tasks from writing code to debugging and validation. Developers find prompt generation challenging, affecting their ability to harness these tools effectively. The tools are adept at boilerplate code generation and code reuse, improving efficiency but also complicating the programming process. Existing metaphors for AI assistance, like search or pair programming, are inadequate, pointing towards a need for new ways to conceptualize AI’s role in programming. Issues of intent specification and code correctness present ongoing challenges, especially in end-user programming contexts.
Writing effective prompts for large language models in programming is challenging, often requiring specific language and structure to yield desirable code outcomes.
The role of programming is shifting towards validating and debugging unfamiliar code generated by AI, rather than creating code from scratch.
AI tools excel at assisting with boilerplate code and reusing existing snippets, enhancing productivity but also introducing new complexities.
Existing metaphors for AI assistance in programming—such as search or pair programming—fall short, necessitating the development of new conceptual frameworks.
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