Tech writers testing the latest generative tools say the secret is not that AI will change everything tomorrow, but that it already helps with specific, everyday tasks — while still making serious mistakes when asked to be an authoritative source. In a recent Verge bonus episode, the publication’s senior reviewer and colleagues described practical uses — from smoothing children’s bedtimes to planning cross-country moves and quickly prototyping game code — but warned the tools “definitely … fall short” in important ways (The Verge). That mixed verdict mirrors peer‑reviewed findings showing large language models (LLMs) can be useful for drafting and brainstorming, yet produce “hallucinated” or fabricated references and factual errors at nontrivial rates when used as research assistants (JMIR study; arXiv survey). For Thai readers — parents, teachers, clinicians and small-business owners — the immediate question is practical: how to use generative AI to save time and spark ideas, while guarding against errors that could mislead decisions in health, education and tourism.
Thailand’s digital landscape and daily life make this balance urgent. With internet penetration above 85–88% and a mobile-first population, AI tools are already reachable to tens of millions of Thai users and rapidly entering workplaces and classrooms (DataReportal; Digital 2025 update). The Thai government and industry are building capacity — the national AI strategy and action plan (2022–2027) and local AI initiatives aim to expand adoption while addressing ethics and regulation (AI Thailand strategy; AI Thailand annual report). That policy momentum helps, but real-world testing by journalists and scientists shows users also need clear habits and institutional safeguards.
Independent testing of everyday uses highlights clear strengths. Reporters and reviewers who tried current tools repeatedly found they speed up routine cognitive work: drafting emails and lesson outlines, generating multiple versions of a bedtime story to fit a child’s interests, producing checklists for moving house, or creating a first-pass rule set and stat blocks for a tabletop role‑playing game (The Verge). Coding assistants such as GitHub Copilot have also been shown in controlled experiments to reduce task time and increase developer throughput, indicating AI copilots can raise productivity in software work that underpins Thailand’s growing digital sector (arXiv Copilot study; GitHub research summary).
But usefulness has limits when accuracy matters. A comparative analysis published in JMIR asked ChatGPT (GPT‑3.5 and GPT‑4) and Bard (Google/Gemini) to find randomized clinical trial references used in systematic reviews on rotator cuff disease. The result: models produced convincing bibliographic citations, but many were fabricated. Hallucination rates ranged from roughly 29% for GPT‑4 to over 91% for Bard in that study, and precision in reproducing references from real systematic reviews was low overall (JMIR study). The authors concluded: “LLMs … should not be used as the sole or primary means for conducting systematic reviews of literature” and must be coupled with rigorous human verification (JMIR study conclusion). Broader literature confirms hallucination is a central technical challenge for LLMs and that the community is actively working on classification and detection methods to reduce erroneous outputs (arXiv survey; Nature study on detection methods).
Experts and experienced users offer a consistent practical rule: use AI for ideation and drafting, not as a final authority. In The Verge conversation, the senior reviewer described day‑to‑day examples where AI “helped bedtime go more smoothly for parents” or “vibe-coded an app for a tabletop role‑playing game,” but panelists repeatedly returned to the need to double‑check facts and sources (The Verge). Researchers behind the JMIR paper emphasized the models’ ability to format bibliographic details accurately when the paper exists, but warned that models frequently invent plausible‑looking citations and may fail to respect search eligibility criteria unless guided by very specific prompts (JMIR study methods and findings). Developers and policy analysts argue the same: progress in model design (better grounding, retrieval‑augmented generation, uncertainty estimation) will reduce hallucinations, but only layered technical and institutional controls will fully protect high‑stakes uses (Nature detection methods).
For Thailand, these findings carry concrete implications across sectors. In healthcare, clinicians and researchers should treat LLM outputs as drafting tools, not literature searches. The JMIR study shows risk is especially acute when AI is used to retrieve or cite medical evidence: fabricated references could mislead clinicians or appear in drafts of scientific manuscripts unless human verification is mandatory (JMIR study). Hospitals and medical schools should therefore adopt protocols requiring clinicians to validate any AI-proposed citations against PubMed or national health databases and to record the verification step in research workflows. For patient-facing uses — chat assistants for triage or health information — health authorities should require explicit disclaimers and escalation paths to qualified professionals.
In education, teachers can gain efficiency by asking LLMs for lesson-plan drafts, assessment ideas and differentiated explanations, but must review materials for accuracy and cultural fit. Thai classrooms often value respect for authority and curricula approved by educational institutions; uncontrolled AI outputs could introduce factual errors or culturally insensitive phrasing. Ministry of Education pilots that combine teacher training in prompt design with institutional review of AI‑generated content can reduce risk and preserve pedagogical standards. Likewise, higher education should teach students how to use LLMs ethically — including how to verify AI-generated sources and when to cite human oversight.
Tourism and small business owners in Thailand can use AI for itinerary planning, multilingual customer responses and creative marketing copy. Given Thailand’s strong tourism recovery and small‑business ecosystems, these tools promise time savings. But businesses should check practical details (opening hours, permit requirements, local regulations) against official sources, because AI can invent specifics like addresses or license numbers that sound plausible but are false.
Cultural context matters in how Thai users respond to AI. Thailand’s family‑centred culture means parents may readily adopt AI prompts that promise convenience (e.g., bedtime stories), while the cultural tendency to defer to perceived authority — including polished digital outputs — could increase the risk of accepting AI assertions without question. Buddhist values that emphasize social harmony may also discourage public confrontation when an AI output is wrong; institutions should therefore build gentle verification habits into common workflows and promote a culture of curiosity rather than blind trust.
Looking ahead, technical and policy trends offer both relief and new tasks for Thailand. On the technical side, improvements in retrieval‑augmented systems (which combine an LLM with verified document stores), model uncertainty estimators and automated hallucination detectors are active research priorities (Nature detection methods; arXiv survey). Commercial coding assistants have shown clear gains in developer productivity in several studies, suggesting Thailand’s software sector can safely integrate such tools alongside code review and testing processes (arXiv Copilot study). On the policy side, Thailand’s national AI strategy provides a framework to scale beneficial uses while addressing ethics and legal safeguards; the challenge is to implement sectoral regulations (health, education, finance) that demand provenance, human oversight and disclosure (Thailand AI strategy; AI Thailand annual report).
For Thai institutions and everyday users, practical steps can make AI adoption safer now. First, adopt a simple verification rule: treat any factual claim, citation or numeric detail from an LLM as “draft” until verified against an authoritative source (PubMed for medicine, government portals for regulations, company websites for business details). Second, require disclosure in research and clinical documentation when AI tools helped produce text or literature searches, and log the verification steps taken (a culture of transparency helps accountability). Third, train frontline professionals — teachers, nurses, civil servants — in prompt design and in spotting common AI errors; specific prompt templates that specify scope, date cutoffs and required output format reduce hallucination risk (JMIR methods). Fourth, for consumer and patient uses, insist on human escalation paths: chatbots that offer medical advice should automatically recommend seeing a clinician or connecting to licensed advice for anything beyond basic information. Fifth, encourage R&D collaborations between Thai universities, industry and government to evaluate LLM behaviour in Thai languages and local contexts; most model evaluations focus on English and Western datasets, so nation‑specific testing is essential.
In the near term, Thai families can safely experiment with AI for everyday convenience — using tools to draft school permission notes, generate practice exam questions, or make dining plans — but should keep a verification habit for health or legal decisions. Educators can use AI to generate differentiated lesson materials while maintaining teacher review as the final step. Hospitals and research groups should prohibit the unverified insertion of AI‑generated citations into manuscripts or patient records and should update institutional policies to require explicit human validation.
Generative AI is neither panacea nor imminent catastrophe; it is a turbocharged set of assistants that perform best when steered by human judgment. The Verge testers capture the day‑to‑day reality: AI can “help bedtime go more smoothly” and make routine creativity less tedious, but it “falls short” when asked to be an authoritative librarian or clinician (The Verge). Peer‑reviewed research warns that hallucinated facts and fabricated references are real risks in medical and academic contexts (JMIR study), and global research is racing to provide technical fixes while regulators build guardrails (arXiv survey; Nature detection methods). For Thailand, the path is clear: adopt AI where it demonstrably saves time, mandate human verification where accuracy matters, and invest in digital literacy so citizens can reap benefits without falling prey to convincing-sounding errors.
Sources: The Verge podcast bonus episode on practical AI uses (The Verge); comparative study of ChatGPT and Bard for systematic review references (JMIR); surveys and technical work on hallucinations and detection (arXiv survey; Nature detection methods); GitHub Copilot impact studies (arXiv Copilot study; GitHub research summary); Thailand digital and policy context (DataReportal Digital 2024: Thailand; Thailand National AI Strategy; AI Thailand Annual Report 2023).