I find the word "engineering" used in this context extremely annoying. There is no "engineering" here. Engineering is about applying knowledge, laws of physics, and rules learned over many years to predictably design and build things. This is throwing stuff at the wall to see if it sticks.
Words often have multiple meanings. The “engineering” in “prompt engineering“ is like in “social engineering”. It’s a secondary, related but distinct meaning.
For example, Google defines the second meaning of "engineering" as:
2. the action of working _artfully_ to bring something about. "if not for his shrewd engineering, the election would have been lost"
Look up “engineering” in almost any dictionary, and it will list something along those lines as one of the meanings of the word. It is a well-established, nontechnical meaning of “engineering”.
Your posted definitions contradict your conclusion - I would argue there is nothing calculated (as parent poster said, there is no calculation, it just trying and watching what works), artful or skillful (because it's so random, what skill is there to develop?) about "prompt engineering".
And in fact, the first engines were developed without a robust understanding of the physics behind them. So, the original version of 'engineering' is more closely to the current practices surrounding AI than the modern reinterpretation the root comment demands.
(1712) Newcomen Engine
(1776) Watt Engine
(1807) Atomic theory of Gasses (Dalton)
(1807) Concept of Energy (Young)
(1824) Carnot Cycle
(1834) Ideal Gas Law (Clayperon)
(1845) Relationship between work and heat (Joule)
(1840-60s) Laws of Thermodynamics (Carnot, Clausius, Kelvin)
For over a century, there were a group of people working on building, maintaining, repairing, refining and improving engines, called 'engineers', who had a very incomplete picture of the physical laws surrounding them. I would assume there were many explosions and other accidents along the way (as there continue to be).
The investment in the science of thermodynamics and the chemistry of fuels was largely motivated by the value of the steam engine, and the attempts to improve efficiencies allowing miniaturization, enabling locomotives and the railroad boom, and eventually automobiles and powered flight.
I think the era from say 1950..2020 has been a relatively unique period in history where science has been ahead of praxis (though folks in medicine or other fields might not have had that luxury). Recent advancements in AI preceding strong theoretical foundations might be a reversion to the mean.
I still like the Canadian approach that to have a title with the word Engineer in it you have to be licensed by the engineering regulator for the province you work in. The US way of every software dev, mechanic, hvac installer or plumber is an engineer is ridiculous.
Disagree. I think it's valid to describe your work as engineering if it is in fact engineering, regardless of credential. If the distinction is important, call it "<credential name> Engineer". But to simply seize the word and say you can't use it until you have this credential is authoritarian, unnecessary, rent seeking corruption.
Doctors and Lawyers are like this. Maybe something like CPA where you can be an accountant or a certified accountant which you need for something important.
CPA is a great example. I'm a half decent accountant, and it should be legal for me to claim that when applying for a position. But it would be fraud to claim I'm a CPA.
How interesting, most people would hesitate to get on a bridge built by a non credentialed civil engineer or have a heart operation by a non certified doctor or get on a 777 with a non credentialed pilot. It’s almost the title the credential the certification signals something about capability
Sorry but in Canada using the word Engineer near your name also means you take legal responsibility personnaly for your professional acts. We are assermented when we earn the title of Junior Engineer after 4 years of university. Then after a period of a few years in the workplace you can have a sponsor Engineer vouch for you. You pass yet another exam and only then you become an Engineer.
This is not true for most so-called Engineers in the US. Anyone can declare themselves an engineer with no exam, no sponsor, no assermentation and no real legal ties to their shoddy work.
>> This is not true for most so-called Engineers in the US.
>> Anyone can declare themselves an engineer with no exam,
>> no sponsor, no assermentation and no real legal ties
>> to their shoddy work.
I don't think that's correct. While there are exemptions, each state requires anyone offering engineering services to the public to be licensed.
> I still like the Canadian approach that to have a title with the word Engineer in it you have to be licensed by the engineering regulator for the province you work in.
That's just not true.
(Despite what Engineers Canada and related parasites tell you.)
You could make this same argument about a lot of work that fall onto "engineering" teams.
There's an implicit assumption that anything an engineer does is engineering (and a deeper assumption that software as a whole is worthy of being called software engineering in the first place)
Perhaps. My point is that the word "engineering" describes a specific approach, based on rigor and repeatability.
If the results of your work depend on a random generator seed, it's not engineering. If you don't have established practices, it's not engineering (hence "software engineering" was always a dubious term).
Throwing new prompts at a machine with built-in randomness to see if one sticks is DEFINITELY not engineering.
i dont see where a random seed would have any bearing on "a specific approach, based on rigour and repeatability"
the approach uses random seeds, and the rigours make it repeatable.
if im thinking about mechanical engineering, something like the strength of a particular beam or the cycle life of a bearing is a random number. An engineer's job includes making random things predictable, by apply design tools like safety factors, and observability tools. thats why we prefer ductile materials; over brittle ones. both have a random strength around the spec, but one visibly changes before it fails, where the other doesnt. we can design in inspection processes that accounts for the randomness.
all kinds of tuning operations also start with somewhat random numbers and bring them to a spot. for the very contemporary example: training an ML model. start with random weights, and predictably change them until you get an effective function.
i dont think the randomness excludes "prompt engineering" from being engineering. instead, it's the rigour of the process in turning the random inputs into predictable outputs
> Throwing new prompts at a machine with built-in randomness to see if one sticks is DEFINITELY not engineering.
Where does all the knowledge, laws of physics, and rules learned over many years to predictably design and build things come from, if not by throwing things at the wall and looking at what sticks and what does not, and then building a model based on the differences between what stuck and what did not, and then deriving a theory of stickiness and building up a set of rules on how things work?
"Remember kids, the only difference between screwing around and science is writing it down."
-Adam Savage
They come from science. Engineering applies laws, concepts and knowledge discovered through science. Engineering and science are not the same, they are different disciplines with different outcome expectations.
It can perfectly be engineering if you have the right validation process. It is, if you can prove that the given randomness can provide satisfactory results to solve the given problem on 99,995% of the cases, then you have a product that solves a given problem following a typical engineering approach.
I dont think that is a good argument. In chemical engineering world, the provider who just randomly changes formulations would be called "ureliable, shoody, crappy giving us something we not ordered".
It’s not random. You’re setting up a straw man of what such a change would be. You can arrive at the same chemical formulation through different means. You may also need to tweak a formulation to achieve a variant effect, ie increased resilience of decay from uv light or countless other reasons for tweaks that would necessitate a construction change. It’s the construction I used as the analogous engineering component of this, and that is where an engineer designing the production system would need to understand the changes that the formulation (comparative item to formulation in this metaphor being the model and its underlying components, chemicals, methods of arriving at the end product).
I've seen some good arguments recently that software engineering is weird in that computers ARE completely predictable - which isn't the case for other engineering fields, where there are far more unpredictable forces at play and the goal is to engineer in tolerances to account for that.
So maybe "prompt engineering" is closer to real engineering than "software engineering" is!
With distributed systems I'd say network unreliability introduces a good amount of unpredictability. Whether that's comparable to what traditional engineering disciplines see, I couldn't say. Some types of embedded programming, especially those deployed out in the field, might also need to account for non-favorable conditions. But the predictability argument is interesting nonetheless.
Engineers work with non-deterministic systems all the time. Getting them to work predictably within a known tolerance window and/or with a quantified and acceptable failure rate is absolutely engineering.
Same way as any other production model in ML. Or any field that requires quality control. Really, this is not fundamentally different in conceptual approach than implementing any other technology or area of knowledge which is a near verbatim definition of engineering.
Depends on the failure mode and application. But a first approximation is the same way you would for a human output. E.g. process engineering for a support chatbot has many of the same principles as process engineering for a human staffed call center.
I saw a talk by somebody from a big national lab recently, and she was announced as the "facilities manager". I wondered for about 5 seconds why the janitor was giving a talk at a technical conference, but it turns out facility meant the equivalent of a whole lab/instrument. She was the top boss.
Assume for the sake of argument, that this is literally sorcery -- ie communing with spirits through prayer.
_Even in that case_, if you can design prayers that get relatively predictable results from gods and incorporate that into automated systems, that is still engineering. Trying to tame chaotic and unpredictable systems is a big part of what engineering is. Even designing systems where _humans_ do all the work -- just as messy a task as dealing with LLMs, if not more -- is a kind of engineering.
> rules learned over many years
How do you think they learned those rules? People were doing engineering for centuries before science even existed as a discipline. They built steam engines first and _then_ discovered the laws of thermodynamics.
There is engineering when this is done seriously, though.
Build a test set and design metrics for it. Do rigorous measurement on any change of the system, including the model, inference parameters, context, prompt text, etc. Use real statistical tests and adjust for multiple comparisons as appropriate. Have monitoring that your assumptions during initial prompt design continue to be valid in the future, and alert on unexpected changes.
I'm surprised to see none of that advice in the article.
I found Hillel Wayne's series of articles about the relationship between software and other engineering disciplines from a few years fairly insightful [1]. It's not _exactly_ the same topic but a lot of overlap in defining wht is "real engineering".
It’s not engineering if you throw anything together without much understanding of the why of things.
But if you understand the model architecture, training process, inference process, computational linguistics, applied linguistics in the areas of semantics, syntax, and more— and apply that knowledge to prompt creation… this application of knowledge from systemic fields of inquiry is the definition of engineering.
Black box spaghetti-hits-wall prompt creation? Sure, not so much.
Part of the problem is the “physics” of prompting changes with the models. At the prompt level, is it Even Possible to engineer when the laws of the universe aren’t even stable.
Engineering of the model architecture, sure. You can mathematically model it.
It changes with any different flavor of a technology in any field of engineering, at least at the level of abstraction that you’re choosing to engage with the problem. Otherwise, this is just machine learning. It yields to the same conceptual approaches in quality control that require fundamental understanding of the underlying fields of study as any area of implementing technology—pretty much the definition of engineering.
You can no more assume the same exact production flow will produce equivalently for a different LLM model than you could for control of a different molecular compound put into product. If you choose only to consider it at the level of equipment assembly then sure, the basic rules of how you assemble the materials— the “physics”— doesn’t hold. If you do so at the same time that such efforts are informed by knowledge of the relevant fields such as material science and of course chemistry then you’re doing chemical engineering. Maybe you don’t want to call the construction workers engineers— though heck in that field many are! But certainly folks like the ones creating the guide posted are being informed by the exact sort of knowledge in the relevant underlying fields.
Indeed. Engineering is the act of employing our best predictive theorems to manifest machines that work in reality. Here we see people doing the opposite, describing theorems (and perhaps superstitions) that are hoped to be predictive, on the basis of observing reality. However insofar as these theorems remain poor in their predictive power, their application can scarcely be called engineering.
Yes, it was written by a SoTA AGI trained for more than 30 years.
I would like to add that predictable generation defeats the very purpose of generative AI, so prompt engineering in this context will never equate to what engineering means in general.
(They're not an LLM fan; also: I directionally agree about "prompt" engineering, but the argument proves too much if it disqualifies "context" engineering, which is absolutely a normal CS development problem).
> Engineering is about applying knowledge, laws of physics, and rules learned over many years to predictably design and build things. This is throwing stuff at the wall to see if it sticks.
There’s one other type of “engineering” that this reminds me of…
1) Software engineers don't often have deep physical knowledge of computer systems, and their work is far more involved with philosophy and to a certain extent mathematics than it is with empirical science.
2) I can tell you're not current with advances in AI. To be brief, just like with computer science more broadly, we have developed an entire terminology, reference framework and documentation for working with prompts. This is an entire field that you cannot learn in any school, and increasingly they won't hire anyone without experience.
Unless you are going into a legal definition, where there's a global enumeration of the tasks it does, "engineering" means building stuff. Mostly stuff that is not "art", but sometimes even it.
Building a prompt is "prompt engineering". You could also call it "prompt crafting", or "prompt casting", but any of those would do.
Also, engineering also had a strong connotation of messing with stuff you don't understand until it works reliably. Your idea of it is very new, and doesn't even apply to all areas that are officially named that way.
First they came for science: Physics, Chemistry, Biology -vs- social science, political science, nutrition science, education science, management science...
Now they come for engineering: software engineering, prompt engineering...
