The article has some good practical tips and it's not on the author but man I really wish we'd stop abusing the term "engineering" in a desperate attempt to stroke our own egos and or convince people to give us money. It's pathetic. Coming up with good inputs to LLMs is more art than science and it's a craft. Call a spade a spade.
But: Interestingly, the behavior of LLMs in different contexts is also the subject of scientific research.
We don’t have that, yet. For instance experiments show that not all parts of the context window are equally well attended. Imagine trying to engineer a bridge when no one really knows how strong steel is.
For example, his first listed design pattern is RAG. To implement such a system from scratch, you'd need to construct a data layer (commonly a vector database), retrieval logic, etc.
In fact I think the author largely agrees with you re: crafting prompts. He has a whole section admonishing "prompt engineering" as magical incantations, which he differentiates from his focus here (software which needs to be built around an LLM).
I understand the general uneasiness around using "engineering" when discussing a stochastic model, but I think it's worth pointing out that there is a lot of engineering work required to build the software systems around these models. Writing software to parse context-free grammars into masks to be applied at inference, for example, is as much "engineering" as any other common software engineering project.
If you are the cincinnatian poet Caleb Kaiser, we went to college together and I’d love to catch up. Email in profile.
If you aren’t, disregard this. Sorry to derail the thread.
I'm honestly a bit confused at the negativity here. The article is incredibly benign and reasonable. Maybe a bit surface level and not incredibly in depth, but at a glance, it gives fair and generally accurate summaries of the actual mechanisms behind inference. The examples it gives for "context engineering patterns" are actual systems that you'd need to implement (RAG, structured output, tool calling, etc.), not just a random prompt, and they're all subject to pretty thorough investigation from the research community.
The article even echoes your sentiments about "prompt engineering," down to the use of the word "incantation". From the piece:
> This was the birth of so-called "prompt engineering", though in practice there was often far less "engineering" than trial-and-error guesswork. This could often feel closer to uttering mystical incantations and hoping for magic to happen, rather than the deliberate construction and rigorous application of systems thinking that epitomises true engineering.
The problem is - and it’s a problem common to AI right now - you can’t generalize anything from it. The next thing that drives LLMs forward could be an extension of what you read about here, or it could be a totally random other thing. There are a million monkeys tapping on keyboards, and the hope is that someone taps out Shakespeare’s brain.
What would "generalizing" the information in this article mean? I think the author does a good job of contextualizing most of the techniques under the general umbrella of in-context learning. What would it mean to generalize further beyond that?
Most engineering disciplines have to deal with tolerances and uncertainty - the real world is non-deterministic.
Software engineering is easy in comparison because computers always do exactly what you tell them to do.
The ways LLMs fail (and the techniques you have to use to account for that) have more in common than physical engineering disciplines than software engineering does!
I’m going to start a second career in lottery “engineering”, since that’s a stochastic process too.
> Are we still calling this things engineering?
Can you make a thing that’ll serve its purpose and look good for years under those constraints? A professional carpenter can.
We have it easy in software.
Software engineering blurs the lines, sure, but woodworking isn't engineering ever.
But they really shouldn't because obviously scheduling and logistics is difficult, involving a lot of uncertainty and tolerances.
Engineers are not just dealing with a world of total chaos, observing the output of the chaos, and cargo culting incantations that seem to work for right now [1]…oh wait nevermind we’re doing a different thing today! Have you tried paying for a different tool, because all of the real engineers are using Qwghlm v5 Dystopic now?
There’s actually real engineering going on in the training and refining of these models, but I personally wouldn’t include the prompting fad of the week to fall under that umbrella.
[1] I hesitate to write that sentence because there was a period where, say, bridges and buildings were constructed in this manner. They fell down a lot, and eventually we made predictable, consistent theoretical models that guide actual engineering, as it is practiced today. Will LLM stuff eventually get there? Maybe! But right now we’re still plainly in the phase of trying random shit and seeing what falls down.
When the central component of your system is a black box that you cannot reason about, have no theory around, and have essentially no control over (a model update can completely change your system behavior) engineering is basically impossible from the start.
Practices like using autoscorers to try and constrain behaviors helps, but this doesn't make the enterprise any more engineering because of the black box problem. Traditional engineering disciplines are able to call themselves engineering only because they are built on sophisticated physical theories that give them a precise understanding of the behaviors of materials under specified conditions. No such precision is possible with LLMs, as far as I have seen.
The determinism of traditional computing isn't really relevant here and targets the wrong logical level. We engineer systems, not programs.
Trial and error and fumbling around and creating rules of thumbs for systems you don’t entirely understand is the purest form of engineering.
A discipline becomes engineering when we achieve a level of understanding. such that we can be mathematically precise about it. Of course experimentation and trial and error are a fundamental part of that process, but there's a reason we have a word to distinguish processes which become more certain and precise thereafter and why we don't just call anything and everything engineering of some form.
You're right that we're still learning how to use them properly. If someone's purely sitting in front of an all-you-can-eat vibe coding machine and trying to one-shot themselves into a fortune with their next startup, then absolutely, they don't deserve to call themselves an engineer.
But just using AI as an assistive technology does not take away from your abilities as an engineer. Used properly, it can be a significant force multiplier
Ah yes, the God given free parameters in the Standard Model, including obviously the random seed of a transformer. What if just put 0 in the inference temperature? The randomness in llms is a technical choice to generate variations in the selection of the next token. Physical engineering? Come on.
Does that really work? And is it affected by the almost continuous silent model updates? And gpt-5 has a "hidden" system prompt, even thru the API, which seemed to undergo several changes since launch...
While this affects all models it seems, I think the case gets worse for in particular LLMs because I would imagine all backends, including proprietary ones, are batching users prompts. Other concurrent requests seem to change the output of your request, and then if there is even a one token change to the input or output token, especially on large inputs or outputs, the divergence can compound. Also vLLM's documentation mentions this: https://docs.vllm.ai/en/latest/usage/faq.html
So how does one do benchmarking of AI/ML models and LLMs reliably (lets ignore arguing over the flaws of the metrics themselves, and just the fact that the output for any particular input can diverge given the above). You'd also want to redo evals as soon as any hardware or software stack changes are made to the production environment.
Seems like one needs to setup a highly deterministic backend, by forcing non-deterministic behavior in pytorch and using a backend which doesn't do batching for an initial eval that would allow for troubleshooting and non-variation in output to get a better sense of how consistent the model without the noise of batching and non-deterministic GPU calculations/kernels etc.
However then, for production, when determinism isn't guaranteed because you'd need batching and non-determism for performance, I would think that one would want to do multiple runs in various real-world situations (such as multiple users doing all sorts of different queries at the same time) and do some sort of averaging of the results. But I'm not entirely sure, because I would imagine the types of queries other users are making would then change the results fairly significantly. I'm not sure how much the batching that vLLM does would change the results of the output; but vLLM does say that batching does influence changes in the outputs.
The best writing I've seen about this is from Hamel Husain - https://hamel.dev/blog/posts/llm-judge/ and https://hamel.dev/blog/posts/evals-faq/ are both excellent.
Dang, so we don't even know why it's not deterministic, or how to make it so? That's quite surprising! So if I'm reading this right, it doesn't just have to do with LLM providers cutting costs or making changes or whatever. You can't even get determinism locally. That's wild.
But I did read something just the other day about LLMs being invertible. It goes over my head but it sounds like they got a pretty reliable mapping from inputs to outputs, at least?
https://news.ycombinator.com/item?id=45758093
> Transformer components such as non-linear activations and normalization are inherently non-injective, suggesting that different inputs could map to the same output and prevent exact recovery of the input from a model's representations. In this paper, we challenge this view. First, we prove mathematically that transformer language models mapping discrete input sequences to their corresponding sequence of continuous representations are injective and therefore lossless, a property established at initialization and preserved during training. Second, we confirm this result empirically through billions of collision tests on six state-of-the-art language models, and observe no collisions.
The distinction here appears to be between the output tokens versus some sort of internal state?
But my point stands. The non-deterministic nature of LLMs are implementation details, not even close to physical constraints as the parent comment suggest.
If an engineer built an internal combustion engine that misfired 60% of the time, it simply wouldn't work.
If an engineer measured things with a ruler that only measured correctly 40% of the time, that would be the apt analogy.
The tool isn't what makes engineering a practice, it's the rigor and the ability to measure and then use the measurements to predict outcomes to make things useful.
Can you predict the outcome from an LLM with an "engineered" prompt?
No, and you aren't qualified to even comment on it since your only claim to fame is a fucking web app
Whoa, where did that come from?
In general, the more constraints you apply on the solution space via context, the more likely the correct solution is to stabilize.
It also helps to engineer the solution in such a way that the correct solution is also the easiest and this the most likely.
It takes time, but like most skills can be learned.
The definition of engineering, according to people outside the pocket of the llm industry:
> The application of scientific and mathematical principles to practical ends such as the design, manufacture, and operation of efficient and economical structures, machines, processes, and systems.
How do these techniques apply scientific and mathematical principals?
I would argue to do either of those requires reproducibility, and yet somehow you are arguing the less reproducible something is that the more like "physical engineering" it becomes.
There is no evidence offered. No attempt to measure the benefits.
As the author points out, many of the patterns are fundamentally about in-context learning, and this in particular has been subject to a ton of research from the mechanistic interpretability crew. If you're curious, I think this line of research is fascinating: https://transformer-circuits.pub/2022/in-context-learning-an...
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