Verified knowledge

Verified knowledge is a curated corpus of facts that have been confirmed by humans or trusted sources — used to ground LLM answers and to detect hallucinations against a known-good baseline.

By 2026 several production LLM teams maintain verified-knowledge stores separate from their general RAG corpora: high-confidence facts (product specs, pricing, official documentation) curated and timestamped by humans. When a user asks a factual question, the system retrieves from verified knowledge first; only if no match does it fall back to general retrieval or pure model knowledge. The pattern materially reduces hallucination on facts the org cares most about. Stored typically as a graph or key-value layer rather than chunked documents.

When to use verified knowledge

• Customer-facing answers where wrong facts are costly.
• Domain-specific assistants over proprietary product knowledge.

Common mistakes

• Letting verified knowledge go stale — wrong with confidence is worse than uncertain.
• Not tracking what's verified vs unverified in the answer.

FAQ

Related terms

• Retrieval-augmented generation (RAG) — Retrieval-augmented generation (RAG) injects relevant documents into the prompt at query time so the model answers from your data instead of its training memory.
• Grounding — Grounding is any technique that ties a language model's output to verifiable sources — retrieved documents, tool results, structured data — instead of pure memory.
• Hallucination — A hallucination is when a language model produces output that is factually wrong, fabricated, or unsupported, while sounding confident.
• Guardrails — Guardrails are deterministic checks layered around a language model to prevent unsafe, off-topic, or non-compliant outputs from reaching the user.

Last updated: 2026-06-01. Raw markdown: https://promtable.com/glossary/verified-knowledge.md.