Real-time fact-checking is the use of live or recently updated sources to verify claims while an AI system is producing or preparing an answer. It is used to reduce hallucinations, stale information, and unsupported assertions in systems that answer questions about current or high-change topics.
In LLM applications, real-time fact-checking typically combines:
- web search or connected data retrieval;
- source ranking and citation selection;
- claim extraction from a draft answer;
- comparison between claims and retrieved evidence;
- citation display or provenance metadata; and
- refusal or uncertainty when evidence is missing.
It is related to Retrieval-Augmented Generation (RAG), but not all RAG is real-time. A RAG system may retrieve from a static internal index. Real-time fact-checking emphasizes freshness and source verification during the request.
The technique does not guarantee truth. Search results may be low quality, sources may conflict, and the model may still overstate what the evidence supports. Professional systems need citation precision, freshness checks, domain whitelists, and human review for high-stakes use.
Perplexity describes its product as searching the web in real time and returning cited answers in What is Perplexity?.
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