Simon Willison has published his experience on he uses LLMs to write code. It is filled with great tips and nuggets of wisdom. Here are my favourites.
If someone tells you that coding with LLMs is easy they are (probably unintentionally) misleading you. They may well have stumbled on to patterns that work, but those patterns do not come naturally to everyone.
If you assume that this technology will implement your project perfectly without you needing to exercise any of your own skill you’ll quickly be disappointed.
Instead, use them to augment your abilities. My current favorite mental model is to think of them as an over-confident pair programming assistant who’s lightning fast at looking things up, can churn out relevant examples at a moment’s notice and can execute on tedious tasks without complaint.
A crucial characteristic of any model is its training cut-off date. This is the date at which the data they were trained on stopped being collected. For OpenAI’s models this is usually October of 2023. Anthropic and Gemini and other providers may have more recent dates.
This is extremely important for code, because it influences what libraries they will be familiar with. If the library you are using had a major breaking change since October 2023, OpenAI models won’t know about it!
I find LLMs respond extremely well to function signatures like the one I use here. I get to act as the function designer, the LLM does the work of building the body to my specification.
Your responsibility as a software developer is to deliver working systems. If you haven’t seen it run, it’s not a working system. You need to invest in strengthening those manual QA habits.
it’s not about getting work done faster, it’s about being able to ship projects that I wouldn’t have been able to justify spending time on at all.
And my favourite.
LLMs amplify existing expertise