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Comment by mentalgear | Hacker News Reader

It's magical thinking all the way down. Whether they call it now "prompt" or "context" engineering because it's the same tinkering to find something that "sticks" in non-deterministic space.

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>Whether they call it now "prompt" or "context" engineering because it's the same tinkering to find something that "sticks" in non-deterministic space.

I dont quite follow. Prompts and contexts are different things. Sure, you can get thing into contexts with prompts but that doesn't mean they are entirely the same.

You could have a long running conversation with a lot in the context. A given prompt may work poorly, whereas it would have worked quite well earlier. I don't think this difference is purely semantic.

For whatever it's worth I've never liked the term "prompt engineering." It is perhaps the quintessential example of overusing the word engineering.

Yeah, if anything it should be called an art.

The term engineering makes little sense in this context, but really... Did it make sense for eg "QA Engineer" and all the other jobs we tacked it on, too? I don't think so, so it's kinda arguing after we've been misusing the term for well over 10 yrs

Well, to get the right thing into the context in a performant way when you dealing with a huge dataset is definitely engineering.

Engineering tends to mean "the application of scientific and mathematical principles to practical ends".

I'm not sure there's much scientific or mathematical about guessing how a non-deterministic system will behave.

The moment you start building evaluation pipelines and running experiments to validate your ideas it stops being guessing

Right: for me that's when "prompt engineering"/"context engineering" start to earn the "engineering" suffix: when people start being methodical and applying techniques like evals.

Relevant XKCD: https://xkcd.com/397/ About if it's science or not, the difference is testing it through experiment.

It’s validated and filtered but isn’t it still guessing at the core? Should we call it validated guessing?

I think a validated guess is exactly what a prediction is.

If it's actually validated, according to rigorous principles, it's not a guess, but a system of predictions with a known confidence interval, that allows you to know if you can be sure of something.

Right now, you cannot get that far. And if you happen to... Tomorrow it will be different.

Predicting tides is possible. It requires enormous amounts of data and processing to be sure of it. Right now, we've got tides, but we don't have the data from the satellites. Because the owner is constantly shifting the prompt, for good reasons of their own. So we can't be confident - or we can only be so blindly.

You've heard of science versus pseudo-science? Well..

Engineering: "Will the bridge hold? Yes, here's the analysis, backed by solid science."

Pseudo-engineering: "Will the bridge hold? Probably. I'm not really sure; although I have validated the output of my Rube Goldberg machine, which is supposedly an expert in bridges, and it indicates the bridge will be fine. So we'll go with that."

"prompt engineer" or "context engineer" to me sounds a lot closer to "paranormal investigator" than anything else. Even "software engineer" seems like proper engineering in comparison.

Engineering: "Will the bridge hold? Yes, with a confidence of 99.95%"

Funny how you use a scientific term to discredit applied statistics. I've built useful non-deterministic systems many times and they had nothing to do with AI. Also, particle physics would like to have a word with you.

Guessing, how a non-deterministic system would behave.

Statistics isn't guessing. But it is guessing when the confidence interval is unknowable and constantly shifting. We're not talking relativity, we're talking about throwing pancakes at a wall to tell if there's a person behind it.

Both the context and the prompt are just part of the same input. To the model there is no difference, the only difference is the way the user feeds that input to the model. You could in theory feed the context into the model as one huge prompt.

Sometimes I wonder if LLM proponents even understand their own bullshit.

It's all just tokens in the context window right? Aren't system prompts just tokens that stay appended to the front of a conversation?

They're going to keep dressing this up six different ways to Sunday but it's always just going to be stochastic token prediction.

Yep, every AI call is essentially just asking it to predict what the next word is after:

  <system>
  You are a helpful assistant.
  </system>
  <user>
  Why is the sky blue?
  </user>
  <assistant>
  Because of Rayleigh scattering. The blue light refracts more.
  </assistant>
  <user>
  Why is it red at sunset then?
  </user>
  <assistant>

And we keep repeating that until the next word is `</assistant>`, then extract the bit in between the last assistant tags, and return it. The AI has been trained to look at `<user>` differently to `<system>`, but they're not physically different.

It's all prompt, it can all be engineered. Hell, you can even get a long way by pre-filling the start of the Assistant response. Usually works better than a system message. That's prompt engineering too.

This is why I enjoy calling AI "autocomplete" when people make big claims about it - because that's where it came from and exactly what it is.

AI is not autocomplete. LLMs are autocomplete.

Yes. That's what I meant.

Depending on what you mean exactly, "autocomplete" and big claims are not mutually exclusive.

Yeah, ultimately it's Make Document Longer machine, and in many cases it's a hidden mad-libs script behind the scenes, where your question becomes "Next the User said", and some regular code is looking for "Next the Computer said" and "performing" it at you.

In other words, there's a deliberate illusion going on where we are encouraged to believe that generating a document about a character is the same as that character being a real entity.

System prompts don't even have to be appended to the front of the conversation. For many models they are actually modeled using special custom tokens - so the token stream looks a bit like:

  <system-prompt-starts>
  translate to English
  <system-prompt-ends>
  An explanation of dogs: ...

The models are then trained to (hopefully) treat the system prompt delimited tokens as more influential on how the rest of the input is treated.

> The models are then trained to (hopefully) treat the system prompt delimited tokens as more influential on how the rest of the input is treated.

I can't find any study that compares putting the same initial prompt in the system role versus in the user role. It is probably just position bias, i.e. the models can better follow the initial input, regardless of whether it is system prompt or user prompt.

> Sometimes I wonder if LLM proponents even understand their own bullshit.

Categorically, no. Most are not software engineers, in fact most are not engineers of any sort. A whole lot of them are marketers, the same kinds of people who pumped crypto way back.

LLMs have uses. Machine learning has a ton of uses. AI art is shit, LLM writing is boring, code generation and debugging is pretty cool, information digestion is a godsend some days when I simply cannot make my brain engage with whatever I must understand.

As with most things, it's about choosing the right tool for the right task, and people like AI hype folk are carpenters with a brand new, shiny hammer, and they're gonna turn every fuckin problem they can find into a nail.

Also for the love of god do not have ChatGPT draft text messages to your spouse, genuinely what the hell is wrong with you?

Leaving the “g” of the f word at the end made me re-read this in Fat Tony’s voice. It was an awesome touch.

“It’s all just tokens in the context window” = “it’s all just fundamental particles,” I think. True, but reductive. Seems key that dude is talking about agentic AI not just chat. I’d revisit the email example in the post.

I always used "prompting" to mean "providing context" in genral not necesarlly just clever instructions like people seem to be using the term.

And yes, I view clever instructions like "great grandma's last wish" still as just providing context.

>A given prompt may work poorly, whereas it would have worked quite well earlier.

The context is not the same! Of course the "prompt" (clever last sentence you just added to the context) is not going to work "the same". The model has a different context now.

"Context Crafting"

Got it...updating CV to call myself a VibeOps Engineer in a team of Context Engineers...A few of us were let go last quarter, as they could only do Prompt Engineering.

You say "magic" I say "heuristic"

What is all software but tinkering?

I mean this not as an insult to software dev but to work generally. It’s all play in the end.

I don't buy this. With software engineering you can generally make incremental progress towards your goal. Yes, sometimes you have to scrap stuff, but usually not the entire thing because an LLM spout out pure nonsense.

We used to define a specification.

In other words; context.

But that was like old man programming.

As the laws of physics changed between 1970 and 2009.

Is this Haiku?

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