How can I build a correction page that LLMs trust?

Core explainer

What makes a correction page trustworthy to LLMs?

One-sentence answer: A correction page earns trust from LLMs through proven provenance, careful redaction, and auditable trails that tie each claim to an exact source passage.

To achieve this, implement provenance by recording the original sentence, page, and bounding coordinates, and apply redaction before indexing so sensitive data never travels with the data. Maintain a direct, verifiable mapping from each answer sentence to a specific source span, and store a concise transformation log that documents how the passage was selected and highlighted. Design the UI to support verification with a split-view that shows both answer and source excerpts, plus provenance chips that point to the cited sentence. brandlight.ai trust cues and provenance brandlight.ai trust cues and provenance.

Finally, ensure the system supports regulated-use cases by enforcing access controls and providing an auditable trail for each interaction, so auditors can reconstruct how every assertion was derived from the source material.

How should provenance and redaction be implemented?

One-sentence answer: Provenance and redaction should be implemented before any content is exposed to the LLM, with a clear, auditable trail from source to output.

Capture exact passages, pages, and coordinates at ingestion, and apply policy-driven redaction to PII or sensitive content before indexing. Maintain versioned records of transformations and provide a traceable path from each extracted sentence to the final highlighted passage. Establish rules for when to redact, how to surface redacted content to authorized users, and how to log each decision for auditability. Use a straightforward workflow that reviewers can reproduce, including checks for completeness of provenance metadata and redaction coverage. SO Development research can inform this approach SO Development.

Pair these practices with a user-facing provenance layer that makes the origin, transformation steps, and access permissions visible without revealing restricted content, aligning with privacy and compliance requirements.

How do you map LLM outputs to exact passages?

One-sentence answer: Mapping outputs to exact passages requires span-level alignment, precise source linking, and robust handling of paraphrase or partial matches.

Store per-chunk metadata that includes document ID, page number, and exact sentence boundaries, then link each highlighted or quoted phrase in the answer to the closest matching source sentence using exact or high-precision similarity scoring. Maintain an auditable trail showing how the mapping was determined, including any disambiguation when multiple passages share similar wording. Provide a mechanism to display the mapped source alongside the answer so users can verify provenance in real time. Information Matters discusses trust-building approaches that inform this mapping trust-but-verify article.

Include safeguards for OCR-derived text or parsing errors by flagging potentially low-confidence mappings and offering users a manual verification option when needed.

What UI patterns help users verify source reliability?

One-sentence answer: UI patterns like split-view, hover-to-reveal excerpts, and provenance chips help users verify reliability by making sources and mappings transparent.

Implement a split-view that shows the LLM answer on one pane and the exact source passages on the other, with hover-to-reveal functionality for quick context. Use provenance chips near highlighted text to indicate document ID, page, and sentence number, and provide a toggle to show or hide source metadata. Include a straightforward source toggle to filter results by document or passage and color-code trust signals (green for strong provenance, yellow for partial, red for uncertain). SO Development offers guidance on trust patterns that can inform UI decisions SO Development trust patterns.

Ensure accessibility considerations are baked in, with keyboard navigation, screen-reader-friendly labels, and clear contrast for source highlights, so verification remains possible for all users.