Source URL: https://www.theregister.com/2025/01/07/ai_can_write_improved_code_research/
Source: The Register
Title: AI can improve on code it writes, but you have to know how to ask
Feedly Summary: LLMs do more for developers who already know what they’re doing
Large language models (LLMs) will write better code if you ask them, though it takes some software development experience to do so effectively – which limits the utility of AI code help for novices.…
AI Summary and Description: Yes
**Summary:** The text discusses an experiment showcasing how large language models (LLMs) can generate and optimize code through iterative prompting. Although LLMs demonstrate significant potential in enhancing code performance, the necessity for software development expertise highlights limitations for novice programmers. The findings suggest that while prompt engineering can lead to faster and more efficient code, it may also introduce bugs that demand human oversight.
**Detailed Description:**
The text centers around an experiment performed by Max Woolf, a senior data scientist, to assess the code optimization capabilities of LLMs like Anthropic’s Claude. Here are the major points discussed:
– **Initial Findings on LLM Code Quality:**
– Woolf indicates that while LLMs can produce functional code, their initial outputs resemble what a novice programmer might write.
– He tasked Claude with writing Python code aimed at calculating the difference between the smallest and largest numbers that sum to 30 from a list of random integers. The baseline code ran in about 657 milliseconds.
– **Iterative Prompting Improves Code Performance:**
– Claude significantly improved the code’s performance by iteratively prompting it to “write better code.”
– With each iteration:
– The first iteration resulted in a 2.7x speed improvement.
– Further iterations led to increases of 5.1x and later improvements reaching up to 99.7x acceleration, although higher performance also led to newly introduced errors in the code.
– **Role of Prompt Engineering:**
– Woolf explored the concept of prompt engineering—providing the LLM with more guidance and specific instructions, which led to even more sophisticated and faster results but at the cost of more bugs.
– The findings emphasized that good quality prompts were vital, with detailed guidance yielding better outcomes than generic requests.
– **Human Expertise Required:**
– Despite LLMs’ advancements, Woolf argues that a software engineering background is essential to distinguish good code and address domain-specific constraints.
– This perspective is supported by recent research from computer scientists which posits that the substance of prompts is more significant than their arrangement, suggesting experienced developers have an edge in extracting high-quality outputs from LLMs.
– **Conclusion:**
– The overarching conclusion is that while LLMs exhibit promising capabilities in code generation and optimization, they are not a replacement for software engineers. Human intervention is necessary to ensure code quality and rectify bugs introduced through AI-generated solutions.
This analysis is particularly relevant for professionals in AI, software security, and development, as it highlights the evolving capabilities of AI in programming while cautioning against over-reliance on these technologies.