How To Fact Check AI, According To Tech Experts

AI tools like ChatGPT can sound authoritative even when they are completely wrong. That contradiction is why tech experts are now pushing a new literacy: how to fact check AI before trusting its output.

Forbes recently published a guide on fact-checking AI, written with insights from technologists and researchers. The article addresses a growing problem: as generative AI becomes embedded in search, writing, and decision-making, users must learn to separate fact from plausible-sounding fabrication. The core message is straightforward but urgent: never take a language model's word as gospel.

The need for this skill stems from the very nature of large language models. Models like GPT-4 are trained to predict the next word, not to verify truth. They can produce confident, elegant sentences that are completely invented—a phenomenon called hallucination. Studies have shown that even advanced models hallucinate 3–5% of the time on factual queries. With millions of people using ChatGPT for research, homework, and work tasks, the potential for misinformation is enormous.

The guide offers several concrete techniques. First, cross-reference every specific claim—names, dates, statistics—with authoritative sources like academic databases, government sites, or reputable news outlets. Second, ask the AI to cite its sources (some tools can produce links, though they may be faulty). Third, use multiple AI tools on the same question; if they disagree, dig deeper. Fourth, check the AI's own limitations: ask it what its training cut-off date is and what domains it covers. Fifth, be especially skeptical of numbers and quotes, which models tend to fabricate most often.

Named in the article are experts such as Oren Etzioni, former CEO of the Allen Institute for AI, who emphasises that fact-checking is a shared responsibility between developers and users. The guide also references initiatives like AI-generated content watermarking and real-time verification plugins being developed by startups. No specific percentages or dates are given beyond general trends.

This advice arrives as companies like OpenAI, Google, and Microsoft race to embed generative AI into their core products. The risk is not just trivial errors; AI hallucinations have already led to legal citations of nonexistent cases by lawyers, financial losses from inaccurate market summaries, and public health scares from fabricated medical advice. Experts argue that teaching fact-checking is as important as teaching digital literacy a decade ago.

Looking ahead, we can expect more tools to automate part of this process—like retrieval-augmented generation (RAG) systems that ground outputs in verified databases. But until those become standard, the burden remains on the user. The most important takeaway: treat AI like a brilliant but unreliable intern—useful for ideas, but always check the facts.