LLMs can write better code with the right prompts, but novice programmers may struggle to leverage this effectively. An experiment using Anthropic's Claude LLM showed that iterative prompting and prompt engineering can significantly improve code performance.
Large language models (LLMs) can produce better code when prompted, but it requires some software development experience to effectively utilize this capability, which limits its usefulness for novice programmers. Woolf highlights the potential for massive productivity gains if code could be improved simply through iterative prompting, such as asking an LLM to 'make the code better.' However, he also raises concerns about the potential for over-iteration and unintended consequences.
An experiment using Anthropic's Claude LLM demonstrated that it could generate optimized code that was 2.7x faster than its initial output when prompted to 'write better code.' Further iterations led to even greater performance improvements but also introduced errors requiring correction. Woolf suggests that 'prompt engineering,' providing LLMs with more detailed instructions and examples, can significantly enhance code quality. He concludes that asking an LLM to 'write code better' does indeed result in improved code, though the definition of 'better' may vary
Large Language Models Code Generation Prompt Engineering AI Software Development
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