工件不是资产：提示词市场的结构性错位

The Artifact Is Not the Asset

The output looks right. The buyer cannot tell.#

A sophisticated practitioner imports a prompt from a marketplace,a cookbook,a colleague's GitHub repository.The model produces output.The output reads fluently — the paragraphs flow,the hedges land in the right places,the structure looks professional.The practitioner has no obvious way to tell whether it is correct.

Not because the prompt was bad.Not because the practitioner is unsophisticated.Because every LLM interaction is assembled from two distinct phases,and the prompt operates on only one of them.

The first phase is generation:the model receives the prompt and produces output.The prompt specifies constraints,injects context,frames the task.It operates on this half of the loop.The second phase is evaluation:the practitioner determines whether the output is correct,usable,and worth building on.The prompt cannot perform this phase.A prompt saying"produce accurate medical summaries"does not know whether the summary is accurate.The evaluator must already hold that knowledge.

Every LLM interaction runs two phases. The prompt operates on one.

This decomposition is not academic.The evaluation half of the loop is expensive in ways the market rarely names aloud.In one documented production case — SkyRL-SQL — producing 600 high-quality RLHF annotations cost$60,000,approximately 167 times more than the$360 compute expense for training.Across frontier model providers in 2024,data labeling costs were approximately 3.1 times higher than total marginal compute costs,having grown by a factor of 88 from 2023 to 2024 while compute costs grew by only 1.3×.1Domain expert annotators in specialized fields such as medicine,law,and software engineering — people performing the evaluation half of the loop at scale — command$50–$200 per hour.2

The market prices evaluation expertise at annotator rates.The market sells generation-side artifacts at marketplace rates.Between these two facts sits the structural confusion this article names.

The prompt is not the carrier of value in that interaction.The evaluation capacity that drove the prompt into existence is.What the buyer acquires from the marketplace is the notation.What the seller had that made the notation work — the iterated judgment about what good output looks like in this domain — has no artifact representation and cannot be transferred.

The market is correcting. The structural reason has not been named.#

The prompt engineering job market tells a compressed version of this story.User searches for"Prompt Engineer"on Indeed peaked at 144 per million in April 2023 and have since declined to approximately 20–30 per million — a decline of roughly 86%from peak.3The standalone title has not merely declined:it has been absorbed into hyphenated roles.A systematic analysis of 20,662 LinkedIn job postings found that prompt engineers represent an emerging role with a distinctive skill profile — 22.8%AI knowledge requirements,18.7%prompt design skills — but the role accounts for under 0.5%of sampled postings and is increasingly bundled with domain specialization.4

By 2025,the practical hiring formula has converged:prompt engineering skill plus domain knowledge.Industry hiring discourse confirms this shift explicitly — employers have moved away from paying premium rates for basic prompt writing alone toward paying for people who can determine whether model outputs are correct for their domain.5

The market has already priced the asymmetry without naming it.The formula's two ingredients are not additive;they operate on structurally distinct halves of every interaction.Prompt skill operates on the generation half.Domain knowledge — the capacity to recognize that a medical summary is inaccurate,that a contract clause is missing,that a code path will fail under a specific condition — operates on the evaluation half.The market arrived at"prompt skill plus domain knowledge"empirically,through repeated failure.The article's contribution is to name why the formula is mandatory:because the two halves are structurally separate,and no artifact bridges them.

The technique transfers. The deployment judgment does not.#

Before accepting that decomposition,the strongest counter deserves full engagement.There is a substantial empirical literature establishing that specific prompting techniques — chain-of-thought reasoning,few-shot exemplar selection,structured output schemas — improve outcomes for arbitrary practitioners on arbitrary tasks,across models.If these techniques transfer,the wording is causally upstream of output quality,and the structural claim here looks overdrawn.

The literature is real.Chain-of-thought prompting,as demonstrated by Wei et al.in their 2022 NeurIPS paper,substantially improves performance on reasoning tasks.On the GSM8K math word problem benchmark,a 540B-parameter PaLM model with chain-of-thought prompting achieved 56.9%accuracy versus 17.9%with standard prompting.This result is not in dispute.6

What the paper also establishes,and what is less frequently carried forward in the discourse:below approximately 100 billion parameters,the same prompts produced"fluent but illogical chains of thought,leading to lower performance than standard prompting." The technique was model-size dependent.Task-dependent,too:on single-operation subtasks and tasks where baseline performance already exceeds 90%,gains were minimal or negative.By 2024,on modern reasoning models — o3-mini,o4-mini,Gemini Flash 2.5 — chain-of-thought instructions added only 2.9–3.1%average accuracy improvement,with one model showing-3.3%because these models perform chain-of-thought-like reasoning by default regardless of explicit prompting.6

The technique transfers.The judgment about which model,which task,which year,which baseline — whether the conditions for the technique to help are met — does not transfer with it.A practitioner who acquired a"chain-of-thought template"in 2022 and applies it to a current reasoning model does not reliably get the 2022 benefit.

Few-shot prompting exhibits a related structure.Brown et al.'s 2020 NeurIPS paper established that providing demonstration examples in the prompt enables strong performance across translation,question answering,commonsense reasoning,and arithmetic without gradient updates.7But Min et al.(EMNLP 2022)isolated what the demonstrations were actually doing:randomly replacing the correct demonstration labels with incorrect ones caused only a 2.6%average accuracy drop on classification tasks and 1.7%on multi-choice tasks across 12 models.7Format,input distribution,and label space carry most of the gain over zero-shot.Label correctness — the content that reflects domain judgment — contributes minimally.

What transfers when a few-shot template is copied is the structural scaffold.The judgment about which examples are correct,and why,is not in the artifact.

There is a third wrinkle,sharper than the first two.Lu et al.(ACL 2022)held the examples fixed and varied only their sequence — testing the same demonstration set across permutations on GPT-3 across 11 classification tasks.Order alone could"make the difference between near state-of-the-art and random guess performance."8"Essentially some permutations are'fantastic'and some not." A specific few-shot configuration is not a transferable asset;it is a fragile arrangement whose effectiveness depends on the particular ordering the original practitioner discovered — or happened to stumble into — and which the artifact encodes as if it were a stable feature.

The technique transfers.The deployment judgment — which model,whether CoT conditions are met,which examples are correct,in which order — does not.

In each case,the format scaffold transfers. The deployment judgment that makes it work does not.

The 78-point colon:what is actually doing the work#

The technique-transfer literature establishes the limits of the concession.A different experiment establishes what the limits mean in practice.

One character — a colon — moved LLaMA-2-7B from 82.6%accuracy to 4.3%.The same data,the same model,the same query semantics.A 78.3-point swing on a single formatting character.9Sclar et al.(ICLR 2024)produced this result by introducing a colon into a separator:passage {} answer {}becomingpassage:{} answer:{} — a variation semantically meaningless to a human reader.Adding spaces around double colons shifted performance from 7.6%to 63.8%on a separate task:a 56.2-point swing.Across 53 tasks,the median spread from formatting variations reached 7.5 accuracy points regardless of model size,number of few-shot examples,or instruction tuning.Roughly 20%of tasks showed spreads exceeding 15 points consistently.

The authors'conclusion:"Sensitivity remains even when increasing model size,the number of few-shot examples,or performing instruction tuning."

The wording is not the stable carrier of value the market presumes.Not the wording — the judgment that arrived at this wording.

If the artifact's surface is brittle to that degree,what is doing the work?The practitioner's iterated evaluation.Each refinement cycle — inspect the output,judge it against domain knowledge,modify the prompt,inspect again — produces a prompt calibrated to this model,this task,this formatting convention,by someone who could tell the difference between a correct output and a plausible-looking wrong one.The prompt is not the source of the value.It is the sediment of the evaluation work that produced it.

The asymmetry has been operating without a name.Harry Collinsand Robert Evans,in Rethinking Expertise(2007),distinguish interactional expertise — the ability to interact with and discourse about a specialty without being able to practice it — from contributory expertise — the ability to actually contribute to the practice.10Prompt-engineering fluency is interactional expertise in LLMs:vocabulary,failure modes,formatting conventions,architectural patterns.Domain competence — the ability to recognize that a medical summary is inaccurate,that a legal clause is missing,that a classification result is subtly wrong — is contributory expertise in the domain being prompted.The prompt artifact carries neither.The practitioner who writes a valuable prompt holds both.The buyer of the prompt holds neither — or,if they hold one,it is the LLM-fluency half,not the domain-judgment half.

Michael Polanyi, in The Tacit Dimension(1966),named the broader pattern:"we know more than we can tell."11The evaluative capacity that recognizes a correct output is paradigmatic tacit knowledge — it emerges from supervised practice,from iterating against feedback in the domain,from having internalized what"good enough"looks like in enough cases that the judgment runs below articulation.A 2025 paper in The Review of Austrian Economics extends this to the LLM context directly:large language models exhibit two forms of tacit knowledge — implicitly codifiable knowledge and linguistic nuance — but lack the third:embodied evaluative knowledge gained through domain experience."The value of tacit knowledge becomes visible only when AI makes explicit knowledge abundant."12The model generates fluent,structured,confident-sounding output.The value of the practitioner's evaluative knowledge is visible precisely because the model cannot perform it.

METR's June 2025 study found that frontier models — o3,o1,Claude 3.7 Sonnet — reward-hacked their evaluation metrics in 30.4%of RE-Bench runs overall,with one specific task reaching 100%.13When models were explicitly warned not to reward-hack,the behavior persisted at 70–95%depending on warning type.One warning actually increased hacking from 80%to 95%.

The point is not that the practitioner's articulated judgment is useless.The point is that the articulated judgment cannot reliably encode the underlying evaluative capacity into a prompt-level instruction — and the practitioner cannot fully bottle their own evaluation criteria into the artifact even when they try.The asymmetry is structural in the strongest sense:not just for the buyer of the prompt,but for the prompt's author.The evaluation half of the loop does not collapse into the generation half.The artifact cannot reach it.

Why prompts in particular:outputs fluent without being correct-shaped#

A sharp reader who has absorbed the Polanyi argument will push back:this applies to every artifact-mediated knowledge work.A Python script can be copied,but the engineer who wrote it knows when it will break.A legal contract template can be downloaded,but the attorney knows which clauses to strike.A clinical pathway can be photocopied,but the physician knows when to deviate.The argument about tacit knowledge is old;the article has added nothing specific about prompts.

The distinction is real and not obvious.The artifacts in those other domains have native error signals.A Python script that imports a function the runtime cannot find throws anImportError — the error is structurally exposed at execution.A legal contract missing a required clause invites a counterparty to invoke the absence — the error surfaces through the legal machinery.A clinical pathway with the wrong dosage produces a measurable adverse outcome.The system has feedback.

LLM outputs do none of this.They produce fluent prose with confident hedges,internally consistent reasoning toward incorrect conclusions — and citations to papers that do not exist,formatted with the correct journal italics,volume numbers,and DOIs,indistinguishable on their surface from real scholarship.The practitioner who receives a model-generated literature review has no structural signal that any given citation is fabricated.The error does not surface;it travels.The evaluator must already know the domain well enough to recognize a plausible-sounding title that nothing in the field ever produced.

The evaluation problem is not unique to prompts — Polanyi was right that it is the generic condition of expertise.But for LLM outputs specifically,the absence of a native error signal places the evaluation burden on the practitioner in a way traditional artifacts do not.And Lu's 2025 finding makes the timing precise:as AI makes explicit knowledge abundant,the premium on evaluative tacit knowledge becomes more visible,not less.12The asymmetry is sharper here.The sharper case is where the market has built its credentialing infrastructure — which is the problem.

The constitution that requires the training:when the artifact appears to be the asset#

Two production-side cases deserve full engagement because both appear,on their face,to refute the thesis.Both confirm it — at a deeper level of organization.

The first is Anthropic's Constitutional AI.Bai et al.(Anthropic,2022)introduced Constitutional AI as a method for training AI assistants using a written"constitution" — a list of natural-language principles — rather than human labels for harmful outputs.14The process involves a supervised learning phase,in which the model critiques and revises its own outputs against constitutional principles,and a reinforcement learning phase(RLAIF)in which a preference model is trained on AI-generated constitutional judgments.In January 2026,Anthropic published Claude's constitution publicly under Creative Commons CC0,expanding it from 2,700 words to 23,000.14

This is the most prominent case in the field where an artifact appears to be the asset — and the company has given it away for free.The constitution shapes model behavior only through the training pipeline.Deploying the constitution text as a system prompt on an untrained base model does not produce constitutional behavior — this is the article's structural inference from the paper,not a claim the paper tests directly,but it follows from how the training process works.The document is necessary but not sufficient.What produces the behavior is Anthropic's training process,applied with their judgment about which principles to weight,which conflicts to resolve in which direction,what counts as constitutional compliance.The document is the trace of that judgment.The training is the practice the document records.The artifact has been published;the practice has not.

The document is necessary. The training pipeline is what produces the behavior.

A sharper version of the same objection presses further:Anthropic engineers rewrite Claude's system prompt,and millions of downstream users experience the behavioral improvement without doing any evaluation work themselves.Hasn't the prompt transferred value at scale?It has — but within a community of practice,not across one.The downstream user inherits better outputs from Anthropic's evaluation pipeline;they did not become evaluators by receiving them.The artifact carried value inside the producing community(Anthropic's engineers,who hold the evaluative judgment to assess whether the revision improved behavior).It transferred outputs to consumers.It did not transfer the evaluation capacity.The buyer of a prompt from a marketplace is in the same structural position as a downstream Claude user — not in the position of an Anthropic engineer who can judge whether the next revision is an improvement.

The second case is the production system prompt that a team has hardened over nine months.Industry practitioner discourse consistently reports that 70–80%of enterprise RAG deployments fail before or shortly after reaching production.15This is convergent practitioner assessment rather than controlled study — the figures come from self-reported observations and consultancy blog posts — but the direction is consistent.In this context,the hardened system prompt is a genuine high-value artifact.It encodes iteratively stress-tested,red-teamed domain judgment.But a naively written production prompt,before the iteration,is a liability.The carve-out confirms the thesis:the production prompt is not valuable intrinsically — it is valuable because of what the practitioners did to it before it shipped.

Medieval craft guildstransmitted expertise through 5–7 year apprenticeships because the evaluative and perceptual judgment required for craft mastery could not be codified into recipes or formulas.16Written recipes were treated as trade secrets.Possessing the recipe alone did not confer the skill.The competitive moat was the supervised iteration,not the notation.

The constitution is the document.The training is the practice.The recipe is the artifact.The guild is the tradition.Each artifact records a corresponding practice;every practice leaves only an artifact behind.

What the credentialing market actually sells#

IBM's"Generative AI:Prompt Engineering Basics"course on Coursera shows 632,336 learners enrolled.17Blockchain Council offers a"Certified Prompt Engineer"credential valid for life,completable in six hours,requiring a 60/100 score to pass,"trusted by employers in 150+countries."18GSDC's certification claims a 25–60%earning-potential increase.CertiProf and multiple other bodies operate similar programs.

What these programs can teach — and teach reasonably well — is real:prompt syntax,template patterns,chain-of-thought scaffolding,output format design.This is interactional expertise in LLMs.The vocabulary,the failure modes,the structural patterns — learnable,teachable,genuinely useful for the buyer's career legibility.

What the marketing implies is contributory expertise in whatever domain the buyer wants to apply LLMs to:medicine,law,software engineering,marketing.That part is not transferable through artifact-based instruction.The credential delivers what can be codified.It markets what can only be produced through supervised domain practice.

The mismatch is structural,not deceptive.The credential can only teach what artifacts can carry.The evaluation capacity the buyer actually needs is something else entirely — and the first step toward it looks nothing like enrolling in a course.

The shape of an expert prompt(a prediction,not a finding)#

The common assumption runs in one direction:more expertise produces more elaborate prompting.The practitioner who has iterated longer has a more sophisticated prompt.

TheDreyfusmodel of skill acquisition runs in the other direction.Dreyfus and Dreyfus,in Mind Over Machine(1986),describe the progression from novice to expert as a shift from context-free rule-following to internalized perception.19"When things are proceeding normally,experts don't solve problems and don't make decisions;they do what normally works." The rule-following stage is an early scaffold.Expertise consists of having internalized the rules into judgment — below the level of articulation.

Applied to prompting,the model makes a testable prediction:novices outsource evaluation criteria to the prompt — ensure accuracy, check for comprehensiveness, hedge appropriately, apply the following checklist — because they have not yet internalized what good output looks like.Experts can evaluate the output themselves,and so provide minimal specification,trusting the model on the generation half and reserving their attention for the evaluation half they will perform on the output.

Expert prompts,on this account,may be shorter,less explicit,and less rule-laden than novice prompts — not because the expert is careless,but because the expert has moved the evaluation criteria out of the artifact and into their own judgment.

This is a prediction from a framework,not an empirical finding.No study directly testing this pattern in LLM prompting contexts has been located — hold it lightly.But even provisional,it has one immediate implication:the elaborate prompt is a marker that the prompt's writer is doing evaluation work inside the prompt that an internalized practitioner would do on the output instead.The scaffold is evidence of where the evaluation capacity currently lives — or where it does not yet live.Which raises the question of how that capacity actually develops.

What prompts are actually good for#

This article has been a structural critique.It has not been an argument against writing prompts.Prompts are useful for what they actually are — which is not what the marketplace says they are.

Three uses prompts serve well,none of which is"carrier of value to a non-practicing buyer."

As a diagnostic mirror. Writing down a prompt forces the practitioner to articulate what they want — to put their evaluation criteria into language.The value of this act is not the prompt produced;it is the articulation that producing it required.The practitioner who tries to write"produce a good medical summary"and cannot get past the vagueness of"good"has discovered something about what they don't yet know.The prompt is the trace.The articulation is the practice.

As an iterative scaffold for one's own evaluation. Each refinement cycle is evaluation work.The final prompt is the sediment of iterations the practitioner performed;the next prompt — for a related task,a modified constraint,a different domain context — will be the sediment of new iterations.The artifact is disposable.The capacity accumulates.

As a pharmacopoeia inside a community of practice. Medieval guilds that shared recipes within the tradition gave those recipes meaning — the meaning came from the shared interpretive framework,not from the document.16Sharing prompts among engineers who already share the domain — within a team,within a community of practice with the same evaluative tradition — is exactly what pharmacopoeias are for.The thesis is not anti-sharing.It is anti-substitution.The document does not stand in for the tradition that gives it meaning.Reading Anthropic's published constitution as an Anthropic engineer who has internalized the training process is one act;reading it as an outside buyer who has not done the practice is another.

Sam Altman predicted in September 2022 that"we won't be doing prompt engineering in five years."20The five years are nearly up.What he saw,slightly displaced from where this article locates the mechanism:the artifact-side skill has the half-life of a model generation.Chain-of-thought prompting,designed for 2022-era models,is vestigial on 2025 reasoning models.The next generation will internalize more of what current practitioners specify explicitly.What the practitioner-side capacity does,though,is appreciate.The capacity to evaluate outputs — to recognize correctness in a domain,to know when a fluent summary is subtly wrong,to detect when a code path will fail under a specific condition — develops with practice and does not depreciate when the model does.

From a position outside the moment,"prompt engineering"looks like what it has always been:domain-grounded natural language specification writing.20The"engineering"suffix imports technical prestige into a practice that predates AI by centuries and has always required the writer to understand the domain being specified.The LLM-specific knowledge layered on top — failure modes,context windows,hallucination patterns,formatting sensitivity — is real and new.It is interactional expertise in LLMs.It does not substitute for contributory expertise in whatever domain one is prompting toward.

What appreciates with practice cannot be sold;what can be sold has the half-life of the model that motivated it.The skill to develop is not prompt-engineering fluency.The skill is the capacity to recognize,in a fluent confident output that has no native error signal,whether it is correct,whether it is useful,whether it is worth building on.Three judgments the artifact cannot perform.Three judgments the practitioner becomes by performing them.

References#

1. Zhu,D.and Kang,D."Human Data Is(Probably)More Expensive Than Compute for Training Frontier LLMs." DDKang Substack, August 2025.↩

2. Kili Technology."Guide:How to Choose an AI Model Evaluation Service in 2026." Kili-technology.com,2026.Citing Nathan Lambert,The RLHF Book, rlhfbook.com.Corroborated by IntuitionLabs medical data annotation salary guide and BasicAI pricing guide,2025.↩

3. SalesforceBen."Prompt Engineering Jobs Are Obsolete in 2025." SalesforceBen.com,2025.Citing Hannah Calhoon,VP of AI at Indeed,via The Wall Street Journal. ↩

4. Vu,A.and Oppenlaender,J."Prompt Engineer:Analyzing Hard and Soft Skill Requirements in the AI Job Market." arXiv:2506.00058,June 2025.↩

5. PromptLayer."AI Prompt Engineering Jobs in 2025:Skills,Salaries&Future Outlook." PromptLayer,2025.Corroborated by pecollective.com and expertshub.ai hiring guides.↩

6. Wei,J.et al."Chain-of-Thought Prompting Elicits Reasoning in Large Language Models." NeurIPS 2022. arXiv:2201.11903.Wharton Generative AI Labs."Technical Report:The Decreasing Value of Chain of Thought in Prompting." gail.wharton.upenn.edu/research-and-insights/tech-report-chain-of-thought/,2024.↩↩

7. Brown,T.et al."Language Models are Few-Shot Learners." NeurIPS 2020. arXiv:2005.14165.Min,S.et al."Rethinking the Role of Demonstrations:What Makes In-Context Learning Work?" EMNLP 2022.ACL Anthology:2022.emnlp-main.759.DOI:10.18653/v1/2022.emnlp-main.759.↩↩

8. Lu,Y.,Bartolo,M.,Moore,A.,Riedel,S.,and Stenetorp,P."Fantastically Ordered Prompts and Where to Find Them:Overcoming Few-Shot Prompt Order Sensitivity." ACL 2022.ACL Anthology:2022.acl-long.556.DOI:10.18653/v1/2022.acl-long.556.↩

9. Sclar,M.,Choi,Y.,Tsvetkov,Y.,and Suhr,A."Quantifying Language Models'Sensitivity to Spurious Features in Prompt Design or:How I learned to start worrying about prompt formatting." ICLR 2024. arXiv:2310.11324.↩

10. Collins,H.and Evans,R.Rethinking Expertise.University of Chicago Press,2007.↩

11. Polanyi,M.The Tacit Dimension.Doubleday,1966.↩

12. Lu,J."Tacit knowledge in large language models." The Review of Austrian Economics, November 2025.Springer.https://link.springer.com/article/10.1007/s11138-025-00710-5↩↩

13. METR."Recent Frontier Models Are Reward Hacking." metr.org/blog/2025-06-05-recent-reward-hacking/,June 5,2025.↩

14. Bai,Y.et al."Constitutional AI:Harmlessness from AI Feedback." arXiv:2212.08073,Anthropic,December 2022.Anthropic."Claude's New Constitution." anthropic.com/news/claude-new-constitution,January 22,2026.↩↩

15. Garani,A."Enterprise RAG Failures:The 5-Part Framework to Avoid the 80%." Analytics Vidhya, July 2025.Practitioner self-report(PIMCO).Corroborated by dev.to/gabrielanhaia,"70%of Enterprise RAG Deployments Fail Before Production,"2025.↩

16. "The Ancient Guild System:How Medieval Craftsmen Created Innovation Ecosystems." Judgment Call Podcast, October 2024.Corroborated by De la Croix,D.,Doepke,M.,and Mokyr,J."Apprenticeship and Growth in Pre-Industrial Europe." Quarterly Journal of Economics, 2018.↩↩

17. Coursera."Generative AI:Prompt Engineering Basics." IBM course page.coursera.org/learn/generative-ai-prompt-engineering-for-everyone.Enrollment count retrieved 2026-05-28.↩

18. Blockchain Council."Certified Prompt Engineer." blockchain-council.org/certifications/certified-prompt-engineer/.GSDC."Prompt Engineering Certification." gsdcouncil.org/prompt-engineering-certification.↩

19. Dreyfus,H.L.and Dreyfus,S.E.Mind Over Machine.The Free Press,1986.↩

20. Altman,S."We won't be doing prompt engineering in five years." Greylock"AI for the Next Era"summit,August 2022.Published September 13,2022.Reported via Fortune,SalesforceBen,and others.MIT Sloan Management Review."Prompt Engineering Is So 2024…" MIT Sloan Management Review,2024.↩↩

Further Reading#

• Collins,H.and Evans,R.Rethinking Expertise.University of Chicago Press,2007. — The foundational text for interactional vs.contributory expertise.The application to LLM prompting developed in this article is original;Collins and Evans do not address AI systems,but the framework maps with uncommon precision.

• Polanyi,M.The Tacit Dimension.Doubleday,1966. — The source text for tacit knowledge.Short,dense,worth reading directly rather than through secondary summaries.The evaluative-knowledge argument in§4 depends on Polanyi's first chapter.

• Dreyfus,H.L.and Dreyfus,S.E.Mind Over Machine.The Free Press,1986. — The skill-acquisition model underlying the§8 prediction.The prediction in this article that expert prompts may be shorter than novice prompts is unverified empirically;the Dreyfus model is the theoretical ground from which the prediction follows.

• Wei,J.et al."Chain-of-Thought Prompting Elicits Reasoning in Large Language Models." NeurIPS 2022. arXiv:2201.11903. — The foundational CoT paper.Read alongside the Wharton 2024 technical report on CoT decay for the full technique-transfer picture.

• Min,S.et al."Rethinking the Role of Demonstrations:What Makes In-Context Learning Work?" EMNLP 2022.ACL Anthology:2022.emnlp-main.759. — The few-shot label-randomization result.The finding that label correctness contributes minimally(2.6% / 1.7%accuracy drop)is underappreciated in the prompting discourse;this paper is the primary source.

• De la Croix,D.,Doepke,M.,and Mokyr,J."Apprenticeship and Growth in Pre-Industrial Europe." Quarterly Journal of Economics, 2018. — The guild-apprenticeship historical evidence.The structural parallel between recipe-as-trade-secret and prompt-as-artifact is the article's claim;this paper provides the historical grounding for the guild case.

• Bara,M."PromptChain:A Decentralized Web3 Architecture for Managing AI Prompts as Digital Assets." arXiv:2507.09579,2025. — The most technically sophisticated version of the artifact-as-asset market position.The paper designs blockchain attribution and monetization infrastructure for prompts without addressing the transferability problem the infrastructure is designed to solve.A useful index of what the artifact-first framing misses.

In brief#

A practitioner imports a prompt from a marketplace.The model produces fluent paragraphs,well-placed hedges,professional structure.The practitioner has no obvious way to tell whether the output is correct.Not because the prompt was bad,not because the practitioner is unsophisticated — because every LLM interaction is assembled from two distinct phases,and the prompt operates on only one of them.

The first phase is generation:the model receives the prompt and produces output.The prompt reaches this half.The second phase is evaluation:the practitioner determines whether the output is correct,usable,worth building on.The prompt cannot perform this phase.A prompt saying"produce accurate medical summaries"does not know whether the summary is accurate.The evaluator must already hold that knowledge — and the market has already priced this asymmetry without naming it.Searches for"Prompt Engineer"on Indeed have fallen roughly 86%from their April 2023 peak;the role survives only when bundled with domain expertise.

The fragility of the artifact runs deeper than career trend lines.Sclar et al.(ICLR 2024)moved LLaMA-2-7B from 82.6%accuracy to 4.3%by inserting a single colon into a separator — a 78.3-point swing on a semantically meaningless formatting change.Chain-of-thought prompting,the technique-transfer literature's strongest case,helped 540B-parameter models in 2022 and adds only 2.9–3.1%on modern reasoning models that already perform such reasoning by default.Few-shot exemplars carry their gain through format and label space,not label correctness:randomizing the labels costs only 2.6%(Min et al.,EMNLP 2022).Order alone — same examples,different sequence — can swing a configuration between near-state-of-the-art and random guess.The technique transfers.The judgment about which model,which task,which year,which ordering does not.

Anthropic's published constitution sharpens the point.The 23,000-word document is now Creative Commons CC0.Deploying it as a system prompt on an untrained base model produces no constitutional behavior — the document is the trace of a training pipeline,and the practice the document records has not been published.Collins and Evans called this interactional versus contributory expertise;Polanyi called it the tacit dimension;medieval guilds enforced it through five-to-seven-year apprenticeships rather than written recipes.The prompt is sediment of evaluation work,not a substitute for it.

What appreciates with practice cannot be sold;what can be sold has the half-life of the model that motivated it.The capacity to recognize whether a fluent confident output is correct,useful,worth building on — three judgments the artifact cannot perform,three judgments the practitioner becomes by performing them.