Comment by BowBun
7 hours ago
I feel like the difference is minimal, if not entirely dismissable. Code in this sense is just a representation of the same information as someone would write in an .md file. The resolution changes, and that's where both detail and context are lost.
I'm not against TDD or verification-first development, but I don't think writing that as code is the end-goal. I'll concede that there's millions of lines of tests that already exist, so we should be using those as a foundation while everything else catches up.
Tests (and type-checkers, linters, formal specs, etc.) ground the model in reality: they show it that it got something wrong (without needing a human in the loop). It's empiricism, "nullius in verba"; the scientific approach, which lead to remarkable advances in a few hundred years; that over a thousand years of ungrounded philosophy couldn't achieve.
The scientific approach is not only or primarily empiricism. We didn't test our way to understanding. The scientific approach starts with a theory that does it's best to explain some phenomenon. Then the theory is criticized by experts. Finally, if it seems to be a promising theory tests are constructed. The tests can help verify the theory but it is the theory that provides the explanation which is the important part. Once we have explanation then we have understanding which allows us to play around with the model to come up with new things, diagnose problems etc.
The scientific approach is theory driven, not test driven. Understanding (and the power that gives us) is the goal.
It most certainly is not. All your tests are doing is seeding the context with tokens that increase the probability of tokens related to solving the problem being selected next. One small problem: if the dataset doesn't have sufficiently well-represented answers to the specific problem, no amount of finessing the probability of token selection is going to lead to LLMs solving the problem. The scientific method is grounded in the ability to reason, not probabilistically retrieve random words that are statistically highly correlated with appearing near other words.
Say you describe your kitchen as “I want a kitchen” - where are the knives? Where’s the stove? Answer: you abdicated control over those details, so it’s wherever the stochastic parrot decided to put them, which may or may not be where they ended up last time you pulled your LLM generate-me-a-kitchen lever. And it may not be where you want.
Don’t like the layout? Let’s reroll! Back to the generative kitchen agent for a new one! ($$$)
The big labs will gladly let you reroll until you’re happy. But software - and kitchens - should not be generated in a casino.
A finished software product - like a working kitchen - is a fractal collection of tiny details. Keeping your finished software from falling apart under its own weight means upholding as many of those details as possible.
Like a good kitchen a few differences are all that stands between software that works and software that’s hell. In software the probability that an agent will get 100% of the details right is very very small.
Details matter.
If it is fast enough, and cheap enough, people would very happily reroll specific subsets of decisions until happy, and then lock that down. And specify in more details the corner cases that it doesn't get just how you want it.
People metaphorically do that all the time when designing rooms, in the form of endless browsing of magazines or Tik Tok or similar to find something they like instead of starting from first principles and designing exactly what they want, because usually they don't know exactly what they want.
A lot of the time we'd be happier with a spec at the end of the process than at the beginning. A spec that ensures the current understanding of what is intentional vs. what is an accident we haven't addressed yet is nailed down would be valuable. Locking it all down at the start, on the other hand, is often impossible and/or inadvisable.