Deterministic processes are great at dealing with objective data, but less great at dealing with free-form text produced by humans.
Each tool should be used for the right job. Until now, we had only cheap plastic tools for language processing. Suddenly, we have a turbo power tool that can parse through pages of English like a hot knife through butter.
We’re all excited by the shiny new tool in the workshop, and we’re putting everything through it just to see what it can do. Eventually the exuberance will subside and we’ll put it to work where it is the most applicable.
That doesn’t mean we’ll abandon other tools and methods.
I've got some bad news for you: natural language has been used to specify programs since before there were computers to run them on.
Design documents, for one. Specifications. Standards documents. Business requests in emails, bug tickets, etc...
It goes on and on. Fundamentally, the process of programming is to convert something from a squishy human language into a purely mathematical one. Right now, programmers perform this task, essentially as "manual labour". Until now , they've had no power tools. That's what LLMs are. They're not autonomous robots -- not yet. Right now, they're levers for the mind, industrial machinery for developers to let them spit out more lines of code per day.
PS: Just as industrial machinery improved product quality, I'm starting to suspect LLMs will be used in the same way. Imagine a coding-specific variant of GPT 4 as a "pair programmer" constantly reading your code and looking for correctness issues, security vulnerabilities, unexpected gotchas, etc...
Yeah well I’ve got bad news for you too: design documents are not computer programs, and even as they exist today, they are prone to misinterpretation and somewhere in the world there’s always a programmer who’s misunderstanding the meaning of a requirement. Plus, the fact that “prompt engineering” is an emerging topic in HN discourse suggests that you need to structure your prompts in a more rigid way so that LLMs can parse them better and potentially faster, which, again, is what programming languages are: constrained and structured means of communicating logic from human to machine.
Each tool should be used for the right job. Until now, we had only cheap plastic tools for language processing. Suddenly, we have a turbo power tool that can parse through pages of English like a hot knife through butter.
We’re all excited by the shiny new tool in the workshop, and we’re putting everything through it just to see what it can do. Eventually the exuberance will subside and we’ll put it to work where it is the most applicable.
That doesn’t mean we’ll abandon other tools and methods.