, the thought has circulated within the AI field that prompt engineering is dead, or not less than obsolete. This, on one side because pure language models have turn out to be more flexible and robust, higher tolerating ambiguity, and alternatively because reasoning models can work around flawed prompts and thus higher understand the user. Regardless of the exact reason, the era of “magic phrases” that worked like incantations and hyper-specific wording hacks appears to be fading. In that narrow sense, prompt engineering as a bag of tricks (which has been analyzed scientifically in papers like this one by DeepMind, which unveiled supreme prompt seeds for language models back when GPT-4 was made available) really is form of dying.
But Anthropic has now put numbers behind something subtler and more essential. They found that while the precise wording of a prompt matters lower than it used to, the “sophistication” behind the prompt matters enormously. Actually, it correlates almost perfectly with the sophistication of the model’s response.
This isn’t a metaphor or a motivational “slogan”, but relatively an empirical result obtained from data collected by Anthropic from its usage base. Read on to know more, because that is all super exciting, beyond the mere implications for a way we use LLM-based AI systems.
Anthropic Economic Index: January 2026 Report
Within the Anthropic Economic Index: January 2026 Report, lead authors Ruth Appel, Maxim Massenkoff, and Peter McCrory analyze how people actually use Claude across regions and contexts. To start out with what’s probably essentially the most striking finding, they observed a powerful quantitative relationship between the extent of education required to grasp a user’s prompt and the extent of education required to grasp Claude’s response. Across countries, the correlation coefficient is r = 0.925 (p < 0.001, N = 117). Across U.S. states, it's r = 0.928 (p < 0.001, N = 50).
Which means that the more learned you’re, and the clearer prompts you possibly can input, the higher the answers. In plain terms, how humans prompt is how Claude responds.
And you realize what? I even have form of seen this qualitatively myself when comparing how I and other PhD-level colleagues interact with AI systems vs. how under-instructed users do.
From “prompt hacks” to “cognitive scaffolding”
Early conversations about prompt engineering focused on surface-level techniques: adding “let’s think step-by-step”, specifying a task (“act as a senior data scientist”), or fastidiously ordering instructions (more examples of this within the DeepMind paper I linked within the introduction section). These techniques were useful when models were fragile and simply derailed — which, by the way in which, was in turn used to overwrite their safety rules, something much harder to realize now.
But as models improved, lots of these tricks became optional. The identical model could often arrive at an affordable answer even without them.
Anthropic’s findings make clear why this eventually led to the perception that prompt engineering was obsolete. It seems that the “mechanical” points of prompting—syntax, magic words, formatting rituals—indeed matter less. What has not disappeared is the importance of what they call “cognitive scaffolding:” how well the user understands the issue, how precisely s/he frames it, and whether s/he knows what a great answer even looks like–in other words, critical pondering to inform good responses from useless hallucinations.
The study operationalizes this concept using education as a quantitative proxy for sophistication. The researchers estimate the variety of years of education required to grasp each prompts and responses, finding a near-one-to-one correlation! This implies that Claude isn’t independently “upgrading” or “downgrading” the mental level of the interaction. As an alternative, it mirrors the user’s input remarkably closely. That’s definitely good when you realize what you’re asking, but makes the AI system underperform while you don’t know much about it yourself or while you perhaps type a request or query too quickly and without being attentive.
If a user provides a shallow, underspecified prompt, Claude tends to reply at a similarly shallow level. If the prompt encodes deep domain knowledge, well-thought constraints, and implicit standards of rigor, Claude responds in kind. And hell yes I’ve definitely seen this on ChatGPT and Gemini models, that are those I exploit most.
Why this isn’t trivial
At first glance, this will sound obvious. After all higher questions recuperate answers. However the magnitude of the correlation is what makes the result scientifically interesting. Correlations above 0.9 are rare in social and behavioral data, especially across heterogeneous units like countries or U.S. states. Thus, what the work found isn’t a weak tendency but a quite structural relationship.
Critically, the finding runs against the common notion that AI could work as an equalizer, by allowing everybody to retrieve information of comparable level no matter their language, level of education and acquaintance with a subject. There’s a widespread hope that advanced models will “lift” low-skill users by mechanically providing expert-level output no matter input quality. The outcomes obtained by Anthropic suggests that this isn’t the case in any respect, and a much more conditional reality. While Claude (and this very probably applies to all conversational AI models on the market) can potentially produce highly sophisticated responses, it tends to achieve this only when the user provides a prompt that warrants it.
Model behavior isn’t fixed; it’s designed
Although to me this a part of the report lacks supporting data and from my personal experience I’d are likely to disagree, it suggests that this “mirroring” effect isn’t an inherent property of all language models, and that how a model responds depends heavily on the way it is trained, fine-tuned, and instructed. Although as I say I disagree, I do see that one could imagine a system prompt that forces the model to all the time use simplified language, no matter user input, or conversely one which all the time responds in highly technical prose. But this is able to have to be designed.
Claude appears to occupy a more dynamic middle ground. Quite than enforcing a set register, it adapts its level of sophistication to the user’s prompt. This design alternative amplifies the importance of user skill. The model is able to expert-level reasoning, but it surely treats the prompt as a signal for a way much of that capability to deploy.
It could really be great to see the opposite big players like OpenAI and Google running the identical sorts of tests and analyses on their usage data.
AI as a multiplier, quantified
The “cliché” that “AI is an equalizer” is usually repeated without evidence, and as I said above, Anthropic’s evaluation provides exactly that… but negatively.
If output sophistication scales with input sophistication, then the model isn’t replacing human expertise (and never equalizing); nonetheless, it’s multiplying it. And that is positive for users applying the AI system to their domains of experience.
A weak base multiplied by a robust tool stays weak, and in the most effective case you should use consultations with an AI system to start in a field, provided you realize enough to not less than tell hallucinations from facts. A powerful base, against this, advantages enormously because then you definitely start with so much and get much more; for instance, I fairly often brainstorm with ChatGPT or higher with Gemini 3 in AI studio about equations that describe physics phenomena, to finally get from the system pieces of code and even full apps to, say, fit data to very complex mathematical models. Yes, I could have done that, but by fastidiously drafting my prompts to the AI system it could get the job done in literally orders of magnitude less time than I’d have.
All this framing might help to reconcile two seemingly contradictory narratives about AI. On the one hand, models are undeniably impressive and may outperform humans on many narrow tasks. However, they often disappoint when used naïvely. The difference isn’t primarily the prompt’s wording, however the user’s understanding of the domain, the issue structure, and the standards for achievement.
Implications for education and work
One implication is that investments in human capital still matter, and so much. As models turn out to be higher mirrors of user sophistication, disparities in expertise may turn out to be more visible relatively than less because the “equalization” narrative proposes. Those that can formulate precise, well-grounded prompts will extract much more value from the identical underlying model than those that cannot.
This also reframes what “prompt engineering” should mean going forward. It’s less about learning a brand new technical skill and more about cultivating traditional ones: domain knowledge, critical pondering, problem decomposition. Knowing what to ask and tips on how to recognize a great answer seems to be the true interface. That is all probably obvious to us readers of , but we’re here to learn and what Anthropic present in a quantitative way makes all of it rather more compelling.
Notably, to shut, Anthropic’s data makes its points with unusual clarity. And again, we must always call all big players like OpenAI, Google, Meta, etc. to run similar analyses on their usage data, and ask that they present the outcomes to the general public similar to Anthropic did.
And similar to we’ve been fighting for a very long time free of charge widespread accessibility to conversational AI systems, clear guidelines to suppress misinformation and intentional improper use, ways to ideally eliminate or not less than flag hallucinations, and more, we will now add pleas to realize true equalization.
References and related reads
To know all about Anthropic’s report (which touches on many other interesting points too, and provides all details concerning the analyzed data): https://www.anthropic.com/research/anthropic-economic-index-january-2026-report
And it’s possible you’ll also find interesting Microsoft’s “Recent Way forward for Work Report 2025”, against which Anthropic’s study makes some comparisons, available here: https://www.microsoft.com/en-us/research/project/the-new-future-of-work/
My previous post “Two Recent Papers By DeepMind Exemplify How Artificial Intelligence Can Help Human Intelligence”: https://pub.towardsai.net/two-new-papers-by-deepmind-exemplify-how-artificial-intelligence-can-help-human-intelligence-ae5143f07d49
My previous post “Recent DeepMind Work Unveils Supreme Prompt Seeds for Language Models”: https://medium.com/data-science/new-deepmind-work-unveils-supreme-prompt-seeds-for-language-models-e95fb7f4903c
