LLMs work best when the user defines their acceptance criteria first (blog.katanaquant.com)

Article summary

The article discusses the limitations of Large Language Models (LLMs) in generating correct code, citing an example of a Rust implementation of a database engine that is 20,000 times slower than the original SQLite code. The author attributes this to LLMs optimizing for plausibility over correctness. The article also highlights the importance of defining acceptance criteria before using LLMs to generate code. The author concludes that LLMs work best when the user defines their acceptance criteria before the first line of code is generated.

Main themes

• LLM limitations
• Code generation
• Performance optimization
• Acceptance criteria
• Software development

What commenters say

• LLMs are not good at tasks they have not seen before and require detailed descriptions to produce decent results.
• The quality of LLM-generated code is highly dependent on the prompt and the model's training data.
• Some argue that LLMs are held to an unreasonably high standard and that their output should be evaluated based on their intended use case.
• Others believe that LLMs lack true understanding and are limited to statistical pattern recognition, which can lead to plausible but incorrect code.
• Defining specific requirements, such as performance constraints, can help improve the quality of LLM-generated code.
• The use of LLMs in software development requires a different set of skills and expectations than traditional coding practices.
• There is a need to distinguish between 'plausible' and 'correct' code, as enterprise customers may prioritize the former over the latter.
• The training data used to develop LLMs can influence their output, and biases in the data can result in suboptimal code generation.