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The new skill in AI is not prompting, it's context engineering

philschmid.de

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Re: The new skill in AI is not prompting, it's context engineering

#421

Post by HSO »

lblume wrote: post #365
v3ss0n wrote: post #334

Earlier quoted context omitted.

At this point , due to non-deterministic nature and hallucination context engineering is pretty much magic. But here are our findings. 1 - LLM Tends to pick up and understand contexts that comes at top 7-12 lines.Mostly first 1k token is best understood by llms ( tested on Claude and several opensource models ) so - most important contexts like parsing rules need to be placed there. 2 - Need to keep context short . W…

I have uploaded entire books to the latest Gemini and had the model reliably accurately answer specific questions requiring knowledge of multiple chapters.

I wonder if those books were already in the training set, i.e. in a way "hardcoded" before you even steered the model that way.

Re: The new skill in AI is not prompting, it's context engineering

#422

Post by bearjaws »

0points wrote: post #313

Only more mental exercises to avoid reading the writing on the wall: LLM DO NOT REASON ! THEY ARE TOKEN PREDICTION MACHINES Thank you for your attention in this matter!

Every time I read something like this, I just imagine it in "old man yells at cloud" meme format.

Just because it is not reasoning doesn't mean it can't be quite good at its tasks.

Re: The new skill in AI is not prompting, it's context engineering

#423

Post by benreesman »

Davidzheng wrote: post #374
benreesman wrote: post #154

The new skill is programming, same as the old skill. To the extent these things are comprehensible, you understand them by writing programs: programs that train them, programs that run inferenve, programs that analyze their behavior. You get the most out of LLMs by knowing how they work in detail. I had one view of what these things were and how they work, and a bunch of outcomes attached to that. And then I spent a…

I highly highly doubt that training a LLM like gpt-2 will help you use something the size of GPT-4. And I guess most people can't afford to train something like GPT-4. I trained some NNs back before the ChatGPT era, I don't think any of it helps in using Chatgpt/alternatives

With modern high-quality datasets and the plummeting H100 rental costs it is 100% a feasible undertaking for an individual to train a model with performance far closer to gpt-4-1106-preview than to gpt-2, in fact its difficult to train a model that performs as badly as gpt-2 without carefully selecting for datasets like OpenWebText with the explicit purpose of replicating runs of historical interest: modern datasets will do better than that by default.

GPT-4 is a 1.75 terraweight MoE (the rumor has it) and that's probably pushing it for an individual's discretionary budget unless they're very well off, but you don't need to match that exactly to learn how these things fundamentally work.

I think you underestimate how far the technology has come. torch.distributed works out of the box now, deepspeed and other strategies that are both data and model parallel are weekend projects to spin up on an 8xH100 SXM2 interconnected cluster that you can rent from Lambda Labs, HuggingFace has extreme quality curated datasets (the fineweb family I was alluding to from Karpathy's open stuff is stellar).

In just about any version of this you come to understand how tokenizers work (which makes a whole class of failure modes go from baffling to intuitive), how models behave and get evaled after pretraining, after instruct training / SFT rounds, how convergence does and doesn't happen, how tool use and other special tokens get used (and why they are abundant).

And no, doing all that doesn't make Opus 4 completely obvious in all aspects. But its about 1000x more effective as a learning technique than doing prompt engineer astrology. Opus 4 is still a bit mysterious if you don't work at a frontier lab, there's very interesting stuff going on there and I'm squarely speculating how some of that works if I make claims about it.

Models that look and act a lot like GPT-4 while having dramatically lower parameter counts are just completely understood in open source now. The more advanced ones require resources of a startup rather than an individual, but you don't need to eval the same as 1106 to take all the mystery out of how it works.

The "holy shit" models are like 3-4 generations old now.

Re: The new skill in AI is not prompting, it's context engineering

#424

Post by SetTheorist »

kazga wrote: post #344
phyalow wrote: post #342

Earlier quoted context omitted.

“non-deterministic machines“ Not correct. They are deterministic as long as a static seed is used.

That's not true in practice. Floating point arithmetic is not commutative due to rounding errors, and the parallel operations introduce non-determinisn even at temperature 0.

Nitpick: I think you mean that FP arithmetic is not _associative_ rather than non-commutative.

Commutative: A+B = B+A Associative: A+(B+C) = (A+B)+C

Re: The new skill in AI is not prompting, it's context engineering

#425

Post by benreesman »

LtWorf wrote: post #414
Davidzheng wrote: post #376

Earlier quoted context omitted.

yes except intelligence isn't like a car, there's no way to break the complicated emergent behaviors of these models into simple abstractions. you can understand a LLM by training one the same amount you can understand a brain by dissection.

I think making one would help you understand that they're not intelligent.

Your reply is enough of a zinger that I'll chuckle and not pile on, but there is a very real and very important point here, which is that it is strictly bad to get mystical about this.

There are interesting emergent behaviors in computationally feasible scale regimes, but it is not magic. The people who work at OpenAI and Anthropic worked at Google and Meta and Jump before, they didn't draw a pentagram and light candles during onboarding.

And LLMs aren't even the "magic. Got it." ones anymore, the zero shot robotics JEPA stuff is like, wtf, but LLM scaling is back to looking like a sigmoid and a zillion special cases. Half of the magic factor in a modern frontier company's web chat thing is an uncorrupted search index these days.

Re: The new skill in AI is not prompting, it's context engineering

#427

Post by parpfish »

pbhjpbhj wrote: post #304
dawnofdusk wrote: post #269

Earlier quoted context omitted.

>This really does sound like Computer Science since it's very beginnings. Except in actual computer science you can prove that your strategies, discovered by trial and error, are actually good. Even though Dijkstra invented his eponymous algorithm by writing on a napkin, it's phrased in the language of mathematics and one can analyze quantitatively its effectiveness and trade-offs, and one can prove if it's optimal (…

Surely claims about context engineering can also be tested using scientific methodology?

yeah, but it's a different type of science.

the move from "software engineering" to "AI engineering" is basically a switch from a hard science to a soft science.

rather than being chemists and physicists making very precise theory-driven predictions that are verified by experiment, we're sociologists and psychologists randomly changing variables and then doing a t-test afterward and asking "did that change anything?"

Re: The new skill in AI is not prompting, it's context engineering

#428

Post by ozgung »

simonw wrote: post #370
wiseowise wrote: post #350

Earlier quoted context omitted.

Because LLMs do no reason. They reply without a thought. Parent commenter, on the other hand, knows when to not engage a bullshit argument. Arguing with “philosophers” like you is like arguing with religious nut jobs. Repeat after me: 1) LLM do not reason 2) Human thought is infinitely more complex than any LLM algorithm 3) If I ever try to confuse both, I go outside and touch some grass (and talk to actual humans)

I agree with your point 2. I can't decide if I agree with your point 1 unless you can explain what "reason" means.

I found few definitions.

"Reason is the capacity of consciously applying logic by drawing valid conclusions from new or existing information, with the aim of seeking the truth." Wikipedia

This Wikipedia definition refers to The Routledge dictionary of philosophy which has a completely different definition: "Reason: A general faculty common to all or nearly all humans... this faculty has seemed to be of two sorts, a faculty of intuition by which one 'sees' truths or abstract things ('essences' or universals, etc.), and a faculty of reasoning, i.e. passing from premises to a conclusion (discursive reason). The verb 'reason' is confined to this latter sense, which is now anyway the commonest for the noun too" - The Routledge dictionary of philosophy, 2010

Google (from Oxford) provides simpler definitions: "Think, understand, and form judgements logically." "Find an answer to a problem by considering possible options."

Cambridge: Reason (verb): "to try to understand and to make judgments based on practical facts" Reasoning (noun): "the process of thinking about something in order to make a decision"

Wikipedia uses the word "consciously" without giving a reference and The Routledge talks about the reasoning as the human behavior. Other definitions point to an algorithmic or logical process that machines are capable of. The problematic concepts here are "Understanding" and "Judgement". It's still not clear if LLMs can really do these, or will be able to do in the future.

Re: The new skill in AI is not prompting, it's context engineering

#429

Post by [deleted] »

passwordqwe wrote: post #417
simonw wrote: post #383

Earlier quoted context omitted.

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.

[deleted]

Re: The new skill in AI is not prompting, it's context engineering

#430

Post by oxidi »

phyalow wrote: post #353
kazga wrote: post #344

Earlier quoted context omitted.

That's not true in practice. Floating point arithmetic is not commutative due to rounding errors, and the parallel operations introduce non-determinisn even at temperature 0.

What? You can get consistent output on local models. I can train large nets deterministically too (CUBLAS flags). What your saying isn't true in practice. Hell I can also go on the anthropic API right now and get verbatim static results.

I think lots of people misunderstand that the "non-deterministic" nature of LLMs come from sampling the token distribution, not from the model itself.
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