# Check Titles: grounding answers at the document level > Check Titles requires title-level grounding, tying answers back to the specific documents they came from for clean attribution. **Category:** Hallucination Prevention **Author:** NeuralSeek Team · **Published:** June 9, 2026 **Canonical:** https://neuralseek.ai/ai-grounded/check-titles **Section index:** https://neuralseek.ai/ai-grounded Trustworthy AI answers can name where they came from. An answer you can trace back to a specific, identifiable document is something a reviewer can verify in seconds; an answer stitched together from anonymous fragments is something you simply have to take on faith. Check Titles enforces grounding at the document-title level, ensuring every response is genuinely anchored to real, named sources rather than assembled from loose pieces that no longer point anywhere. ## What it actually does The guardrail verifies that the answer aligns with the titles of the documents it claims to draw from. When the content can be tied back to a real source title, the answer is treated as well-grounded and the attribution travels with it. When it can't — when the answer's substance doesn't correspond to any identifiable document — it's treated as weakly grounded and handled accordingly, whether that means flagging, suppression, or routing to a fallback. ## Why business teams care Attribution is the visible face of trust. When an answer can point to 'this came from this specific document,' reviewers, auditors, and end users can confirm it almost instantly, and the assistant stops being an opaque oracle and becomes a transparent researcher that shows its sources. In regulated industries, that traceability isn't a nicety — it's frequently the difference between an answer you can defend and one you can't. ## How to tune it in practice Check Titles is most powerful when your knowledge base has clean, meaningful document titles, so the first practical step is ensuring titles actually describe their contents. From there, decide how strict the requirement should be: in high-assurance workflows, insist that every answer carry verifiable title-level grounding; in lighter-weight tools, you might allow title grounding to inform confidence without hard-blocking. The cleaner your titling, the more signal this guardrail can extract. ## Common failure modes it prevents The main failure it addresses is the 'sourceless summary' — a fluent answer that reads as authoritative but can't actually be tied back to any identifiable document. It also helps surface mismatches where an answer drifts away from the document it cites, since the title alignment check exposes the gap. Both are failures that erode trust quietly, because nothing about the answer's surface reveals the problem. ## Where it fits in the stack Check Titles works alongside Check URLs and the coverage controls to build the attribution layer of grounding. Where the semantic threshold asks 'is this answer supported?', Check Titles asks 'can we name what supported it?' — and the two together produce answers that are not only correct but demonstrably, verifiably sourced. That combination is what makes the audit trail genuinely useful rather than merely present. ## Audit-ready by design Because each grounded answer is associated with named sources, the audit trail captures not just what was said but what it was based on. When a regulator, a customer, or an internal risk team asks 'where did this come from?', the answer is a record with document titles attached — exactly what compliance reviews require, produced automatically rather than reconstructed after the fact. > An answer that can't name its source is an opinion. An answer that can is evidence. ## The takeaway Check Titles anchors answers to identifiable documents, making every response attributable, verifiable, and audit-ready — turning the assistant from an opaque oracle into a transparent, sourced researcher. --- From NeuralSeek's AI Grounded — practical, web-verified guidance on building governed, grounded enterprise AI. NeuralSeek is the model-agnostic, governed AI platform you own: any LLM (swap with no rebuild), your data in your own tenant (cloud or on-prem), 118 guardrails enforced before any action, one container that runs anywhere.