Are questions asked on Factually answered by AI?
Executive summary
Factually describes itself as "Your personal AI fact‑checker" that delivers verified facts via messaging platforms like WhatsApp and Telegram, which implies that user questions submitted to Factually are answered by an AI system [1]. That claim sits inside a wider ecosystem of AI fact‑checking tools that use either pre‑trained models or live web connections and that carry known strengths and limits — including hallucination risk and the need for lateral reading — so any answer from Factually should be treated as an AI output requiring verification [2] [3] [4].
1. How Factually presents itself: an AI answering user queries
The Factually product page explicitly markets the service as an AI-driven personal fact‑checker where users "send news, links, or quotes" and "get verified facts instantly via WhatsApp and Telegram," a direct statement that questions submitted to Factually are handled by AI rather than purely human reviewers [1]. That self‑description is the clearest piece of evidence available in the reporting: it is how Factually represents its core functionality to potential users [1].
2. Where Factually fits among AI fact‑checkers
Factually is one of many tools positioning AI as the engine of verification; comparable services and features are described across the market, from platforms that claim live internet access to generate up‑to‑date answers (FactsGPT, LongShot) to organizational projects that combine human fact‑checkers with AI systems (Full Fact) [2] [5] [6]. Vendors advertise differing architectures — some emphasizing live web connections for "verified, up‑to‑date answers" while others present internally trained checkers designed to flag potential truth statuses — which shows a spectrum of technical approaches to answering user queries [2] [7] [5].
3. Accuracy caveats: composite outputs and hallucinations
Across academic and library guidance, a consistent fact emerges: AI outputs are composites of multiple, often unidentifiable sources and can confidently state inaccuracies — a phenomenon librarians and universities warn users to guard against with lateral reading and claim‑level verification [3] [8] [9]. Practical help pieces reiterate the same: automated tools speed workflows but do not eliminate the need to verify claims, because models can "hallucinate" or recycle outdated or biased source material [4] [10]. Applied to Factually, that means even if an AI answers a user's question, the answer inherits the general AI reliability challenges documented by research and practice [3] [4].
4. Signals of trust and the limits of available reporting
Some AI fact‑checking services attempt to increase trust with features such as confidence scores or explicit source citations; researchers and builders recommend these as mitigations because users increasingly rely on AI for information [11] [2]. The public reporting provided here does not detail whether Factually exposes confidence scores, how it sources evidence, or whether humans review outputs, so any claim about the fidelity of its answers beyond the firm's marketing statement would exceed the available documentation [1] [2] [6].
5. Practical implication: yes — but verify
The most defensible conclusion from the available reporting is straightforward: questions asked on Factually are answered by AI according to Factually’s own description of the service [1]; however, the AI fact‑checking field carries documented limitations — composite sourcing, hallucinations, and the need for lateral reading — that mean users should treat those answers as starting points requiring corroboration from authoritative sources [3] [8] [4] [10]. The credible alternative viewpoint — that some platforms combine human oversight or live web retrieval to improve reliability — is reflected in the broader landscape but is not substantiated for Factually in the sources provided [6] [2].