ArticlePloS one2026
Prompt engineering in the age of generative AI: Insights from public discourse on social media.
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
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Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPublic discourse on "prompt engineering" has grown rapidly, but large-scale evidence on how people discuss it is limited. The nascent coinage describes a user journey of optimizing text-based parameters within large language models and generative AI platforms in a trial-and-error process, adding modifiers or key phrases to yield a satisfactory output. While several studies have systematically and taxonomically mapped these techniques, no big data studies have specifically delved into the public reception to the concept. The study significantly contributes to the literature by documenting emerging new trends. It is prudent to keep abreast of discursive content as these techniques enter into the public lexicon, by capturing public discourse, sentiments and dominant themes.
objectivesThis paper maps prominent discussion peaks and quarter-by-quarter themes and sentiment in posts mentioning "prompt engine*" on X and Reddit (1 May 2023-30 April 2024).
methodsWe analyzed a large social media corpus (n = 298,774) of publicly-available English-language posts containing "prompt engine*" (engine/engineer/engineering/engineered). Our first research direction detected discussion spikes using peak-prominence modeling (pre-specified prominence threshold at 95th-percentile significance), then qualitatively coded the top-engagement posts on peak dates. Our second research direction computed quarterly sentiment, frequent bigrams, and emoji usage.
resultsSix peaks centered on: (i) skill validity, (ii) future ramifications, (iii) title longevity, (iv) artist-attribution disputes, (v) model bias/guardrails, and (vi) analogies to child-rearing. Negative sentiment increased from 14.1% (Q1) to 37.3% (Q4), while positive sentiment fell from 36.4% to 22.8%. Tutorials dominated early content; later quarters featured humor, satire and legal liability discussions.
conclusionsPublic narratives around prompt engineering increasingly question legitimacy, longevity, and ethics. Clearer terminology, transparency about guardrails, and guidance on responsible use may help align expectations and practice.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.