Crafting a good prompt is what differentiates an expert's output from a beginner's. Of course you need good constraints too. But those good constraints are created precisely through good prompts.
This is an amazing article. The problems it describes are exactly what we found when building a production system that used LLMs to (most of the time) produce reliable results. Extensive tests are necessary, and stakeholders have no idea how their suggestions fail in production. They just see the handful of times they tried something and had it work, not the long tail of cursed results. (“How hard can it be? Why don’t you just…”)
We didn’t get to the point of self-built prompts, as the article suggests, but it’s an intriguing idea.
I hope the future of AI isn't this sort, where companies provide the user/customer with an interface to an AI that can do things for the user... I would much prefer that companies instead provide an interface FOR an AI, and the user brings their own AI which connects to that interface.
In other words, provide my AI with tools, instead of providing me an AI that uses your tools.
That way, my AI can bring all the context it needs, and I can bring all of the settings and knowledge about what I want with me. I don't want a fractured world of tons of AIs i interact with where I have to explain all the fundamental information about what I want and how I work every time.
This also has the benefit of sidestepping the issue the essay is talking about. You provide a consistent tool, and the AI weirdness is not your issue anymore. You don't have to worry about solving for all the weird ways people prompt the AI, or the ways they break.
I don’t think that will happen. It sounds good, and I would like it if things operated that way - but from the company perspective how do they, for example, have a tool call that gives the customer a discount, without it getting used when it shouldn’t?
Companies want ai to replace human customer service decision making, which means it can’t just be an api that an external agent can interact with, because it needs private knowledge of company processes and access to capabilities that are abusable.
But we’re already at the point where if you manage to talk to a human, mostly you end up speaking to someone with no actual power to resolve your issue - so i think basically the future is just going to suck
i am not reading this. it's already a terrible format to begin with and the writer of course is the typically sort that thinks anyone cares for his insipid humor and his bio. this article is trash regardless of the shreds of actual content might be.
the textual nature of prompts leads us to take the intentional stance towards systems which aren’t conscious, and thus miss the essential nature of their non-meaning
I see LLMs as being capable of making useful distinctions and having a rich action space. They are widely used because their operation is useful, and that can only happen when semantics work well in practice.
Crafting a good prompt is what differentiates an expert's output from a beginner's. Of course you need good constraints too. But those good constraints are created precisely through good prompts.
This is an amazing article. The problems it describes are exactly what we found when building a production system that used LLMs to (most of the time) produce reliable results. Extensive tests are necessary, and stakeholders have no idea how their suggestions fail in production. They just see the handful of times they tried something and had it work, not the long tail of cursed results. (“How hard can it be? Why don’t you just…”)
We didn’t get to the point of self-built prompts, as the article suggests, but it’s an intriguing idea.
I hope the future of AI isn't this sort, where companies provide the user/customer with an interface to an AI that can do things for the user... I would much prefer that companies instead provide an interface FOR an AI, and the user brings their own AI which connects to that interface.
In other words, provide my AI with tools, instead of providing me an AI that uses your tools.
That way, my AI can bring all the context it needs, and I can bring all of the settings and knowledge about what I want with me. I don't want a fractured world of tons of AIs i interact with where I have to explain all the fundamental information about what I want and how I work every time.
This also has the benefit of sidestepping the issue the essay is talking about. You provide a consistent tool, and the AI weirdness is not your issue anymore. You don't have to worry about solving for all the weird ways people prompt the AI, or the ways they break.
I don’t think that will happen. It sounds good, and I would like it if things operated that way - but from the company perspective how do they, for example, have a tool call that gives the customer a discount, without it getting used when it shouldn’t?
Companies want ai to replace human customer service decision making, which means it can’t just be an api that an external agent can interact with, because it needs private knowledge of company processes and access to capabilities that are abusable.
But we’re already at the point where if you manage to talk to a human, mostly you end up speaking to someone with no actual power to resolve your issue - so i think basically the future is just going to suck
Companies will do whatever creates the most lock-in for the user and generates the most profit for themselves.
Ask yourself what's in their best interest as a business? That's probably what they'll do.
TL;DR: you need to do... essentially supervised learning on your prompts? I mean, if I wanted to do ML, I'd already have been doing it ten years ago.
i am not reading this. it's already a terrible format to begin with and the writer of course is the typically sort that thinks anyone cares for his insipid humor and his bio. this article is trash regardless of the shreds of actual content might be.
I see LLMs as being capable of making useful distinctions and having a rich action space. They are widely used because their operation is useful, and that can only happen when semantics work well in practice.