An LLM Approach

Published on: {pubDate}

Simon Willison on his approach to using LLMs for production code:

Once I’ve completed the initial research I change modes dramatically. For production code my LLM usage is much more authoritarian: I treat it like a digital intern, hired to type code for me based on my detailed instructions.

Here’s a recent example:

Write a Python function that uses asyncio httpx with this signature:

async def download_db(url, max_size_bytes=5 * 1025 * 1025): -> pathlib.Path

Given a URL, this downloads the database to a temp directory and returns a path to it. BUT it checks the content length header at the start of streaming back that data and, if it’s more than the limit, raises an error. When the download finishes it uses sqlite3.connect(…) and then runs a PRAGMA quick_check to confirm the SQLite data is valid - raising an error if not. Finally, if the content length header lies to us - if it says 2MB but we download 3MB - we get an error raised as soon as we notice that problem.

I could write this function myself, but it would take me the better part of fifteen minutes to look up all of the details and get the code working right. Claude knocked it out in 15 seconds.

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.

I’ll often follow-up with “Now write me the tests using pytest”. Again, I dictate my technology of choice - I want the LLM to save me the time of having to type out the code that’s sitting in my head already.

If your reaction to this is “surely typing out the code is faster than typing out an English instruction of it”, all I can tell you is that it really isn’t for me any more. Code needs to be correct. English has enormous room for shortcuts, and vagaries, and typos, and saying things like “use that popular HTTP library” if you can’t remember the name off the top of your head.

The good coding LLMs are excellent at filling in the gaps. They’re also much less lazy than me - they’ll remember to catch likely exceptions, add accurate docstrings, and annotate code with the relevant types.