Does Good Software Engineering Improve the Performance of Large Language Models with Software Tasks?The article discusses how structured, modular software engineering practices enhance the effectiveness of large language models (LLMs) in software development tasks. It emphasizes the importance of clear and coherent code, which allows LLMs to better understand, extend functionality, and debug. The author shares experiences from the Bad Science Fiction project, illustrating how well-engineered code improves AI collaboration.
Key takeaways:
- Modular Code: Use small, well-documented code blocks to aid LLM performance.
- Effective Prompts: Design clear, structured prompts by defining context and refining iteratively.
- Chain-of-Thought Models: Provide precise inputs to leverage structured problem-solving abilities.
- Prompt Literacy: Master expressing computational intent clearly in natural language.
- Iterative Refinement: Utilize AI consultants for continuous code improvement.
- Separation of Concerns: Organize code into server and client roles for better AI interaction.
