It actually still scares the hell out of me that this is the way even the experts 'program' this technology, with all the ambiguities rising from the use of natural language.
Keep in mind that this is not the only way the experts program this technology.
There's plenty of fine-tuning and RLHF involved too, that's mostly how "model alignment" works for example.
The system prompt exists merely as an extra precaution to reinforce the behaviors learned in RLHF, to explain some subtleties that would be otherwise hard to learn, and to fix little mistakes that remain after fine-tuning.
You can verify that this is true by using the model through the API, where you can set a custom system prompt. Even if your prompt is very short, most behaviors still remain pretty similar.
There's an interesting X thread from the researchers at Anthropic on why their prompt is the way it is at [1][2].
Supposedly they use "RLAIF", but honestly given that the first step is to "generate responses... using a helpful-only AI assistant" it kinda sounds like RLHF with more steps.
LLM Prompt Engineering: Injecting your own arbitrary data into a what is ultimately an undifferentiated input stream of word-tokens from no particular source, hoping your sequence will be most influential in the dream-generator output, compared to a sequence placed there by another person, or a sequence that they indirectly caused the system to emit that then got injected back into itself.
Then play whack-a-mole until you get what you want, enough of the time, temporarily.
I think OP's point is that "hope" is never a substitute for "a battery of experiments on dependably constant phenomena and supported by strong statistical analysis."
Honestly, this sort of programming (whether it's in quotes or not) will be unbelievably life changing when it works.
I can absolutely put into words what I want, but I cannot program it because of all the variables. When a computer can build the code for me based on my description... Holy cow.
Well, hopefully your developers are substantially more capable, able to clearly track the difference between your requests versus those of other stakeholders... And they don't get confused by overhearing their own voice repeating words from other people. :p
We all use abstractions, and abstractions, good as they are to fight complexity, are also bad because sometimes they hide details we need to know. In other words, we don't genuinely understand anything. We're parrots of abstractions invented elsewhere and not fully grokked. In a company there is no single human who understands everything, it's a patchwork of partial understandings coupled functionally together. Even a medium sized git repo suffers from the same issue - nobody understands it fully.
Wholeheartedly agree. Which is why the most valuable people in a company are those who can cross abstraction layers, vertically or horizontally, and reduce information loss from boundaries between abstractions.
... or - worse even - something you think is what you want, because you know not better, but happens to be a wholy (or - worse - even just subtly partially incorrect) confabulated answer.-
It still scares the hell out me that engineers think there’s a better alternative that covers all the use cases of a LLM. Look at how naive Siri’s engineers were, thinking they could scale that mess to a point where people all over the world would find it a helpful tool that improved the way they use a computer.
The original founders realised the weakness of Siri and started a machine learning based assistent which they sold to Samsung. Apple could have taken the same route but didn't.
I mean, there are videos from when Siri was launched [1] with folks at Apple calling it intelligent and proudly demonstrating that if you asked it whether you need a raincoat, it would check the weather forecast and give you an answer - demonstrating conceptual understanding, not just responding to a 'weather' keyword. With senior folk saying "I've been in the AI field a long time, and this still blows me away."
So there's direct evidence of Apple insiders thinking Siri was pretty great.
Of course we could assume Apple insiders realised Siri was an underwhelming product, even if there's no video evidence. Perhaps the product is evidence enough?
My overall impression using Siri daily for many years (mainly for controlling smart lights, turning Tv on/off, setting timers/alarms), is that Siri is artificially dumbed down to never respond with an incorrect answer.
When it says “please open iPhone to see the results” - half the time I think it’s capable of responding with something but Apple would rather it not.
I’ve always seen Siri’s limitations as a business decision by Apple rather than a technical feat that couldn’t be solved. (Although maybe it’s something that couldn’t be solved to Apple’s standards)
