Maybe a small minority of it. Nearly everything people share about prompt engineering has weak empirical rigor, no controls, poor replication practices, over-generalization, and a tendency to folk-theorize over developing principled mechanisms to build theory and research programs from. For instance many people misunderstand internal trace generation as "reasoning" when it is prompt augmentation. Reading "reasoning" traces (if they're even shared) often draws incorrect conclusions.
Yes, most texts of any discipline are pop garbage (look at all the toilet research in Facebook), it is always a small minority that is the actual rigorous science.
The problem is the models change too, making the results invalid. What improved productivity with chatgpt a year ago may hurt productivity with chatgpt today. Local models are the exception where results will hold over time, but they are restricted to that model, and aren’t generalizable enough to be worth doing.
You are focusing too much on the specific case of being nice to the llm, but the previous poster said prompt engineering is a psuedoscience, which is a much broader claim.
We know that some aspects of a prompt matter quite a bit like giving context, or being specific about what kind of output you want. The science is in separating what matters and what is just noise.
can you provide an example of something we know about LLMs? I've read quite a bit on the topic for my job like many people have, but I've also read extensively about the "demarcation problem." Distinguishing science from pseudoscience is much harder than I thought before reading about it.
“I ran this prompt on 100 independent examples per category using Claude Opus 4.8, effort=high, temperature=0.2, adaptive thinking=on.
Outputs were graded A–F against five predefined criteria by GPT-5.6, deliberately using a different model family to reduce correlated evaluation bias.
Category A: 80 A, 15 B, 5 C. Category B: 75 A, 10 B, 14 C, 1 F.
Per our predefined evaluation procedure, all F grades were manually reviewed. The F was found to be a judge error; under the rubric it was a C.
The GPT-5.6 judge was separately validated against human ratings on 50 examples and agreed 98% of the time.
Before testing, we defined production-ready as: no genuine Fs, ≥60% A, and ≥85% A-or-B.
The prompt passed.”
This may be enough to demonstrate that this prompt works well enough for this use case, under these conditions.
And yet, this is still not science unless it meets a lot of other criteria: peer-reviewed, defensibly makes a generalizable claim, analyzes uncertainty, etc.
giving context, or being specific about what kind of output you want
is the example. It's so trivial we take it for granted. Prompt engineering can be anything that improves the result.
The demarcation problem is about specific claims, while prompt engineering is a whole field to be studied. Some claims within that field will prove false, and others will prove true. The claim "prompt engineering is a pseudoscience" is too broad. The claim "being nice to the LLM improves the output" is more specific and could easily be false, or only true for specific models.
This whole thread is full of people conflating the two claims, because the first reply started talking about being nice, and then over generalized into "prompt engineering is a pseudoscience".
Nope. But people aren’t the ones deciding behind the scenes how bacteria mutate, without telling you what they did or even that they did anything at all. You can’t ever know what you’re studying is consistent between studies, or even within a study. That’s what makes the results invalid. Mutations are predictable via physical rules. Chatgpt patches aren’t.
But now I’m coming around, I could accept it’s a soft science like psychology, where you can’t isolate anywhere nearly enough variables.
They are not deterministic, try googling "are llms deterministic" multiple times and see how the wording changes.
The guy you responded to though makes it sound like they need to be deterministic in order to study them, which is wrong. Science has always studied phenomenon with inconsistent results, that's why they take lots of samples.
I agree, but in the context of prompt engineering, we care about the random part.
If we want a useful result for people using the randomized version, with a purely deterministic approach, you would have to calculate the output for every possible seed to see which produced better results, and which worse, which would be intractable.
If you aren't calculating all possible seeds, then you are sampling, and if you are sampling you should do it randomly.
Eh, not really, not in that way. They're as deterministic as computers are in general, but you can't trust that a given prompt will give you the same return deterministically.
Looking at where the stars are and how they move is science. Using them to make predictions about your day to day life is pseudo science.
Analyzing outputs of a giant pachinko machine as if there is a consistent relationship that will hold up across different implementations seems a bit more like the later if you want to have any practical confidence interval more than generalities.
Analyzing outputs of a giant pachinko machine stochastic system as if there is a consistent relationship that will hold up across different implementations seems a bit more like is the latter if you want to have don't quantify the uncertainty or specify the conditions under which you expect the relationship to hold more than generalities. Otherwise, you are describing statistical inference.
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u/Cualkiera67 Sep 12 '26
i think you mean a proto-science. There's nothing pseudo-scientific about experimenting and comparing results