Your AI assistant gave your team a policy answer. Someone asked "where did that come from?" and nobody could say.
The answer looked right. But when a decision went wrong, you had no way to trace back to the source that caused the bad recommendation.
You are using AI to surface information, but you cannot show your team which documents it actually pulled from.
An answer without a source is an opinion. Your team needs to know where the information came from before they can trust it.
INTERMEDIATE - Builds on chunking and embeddings to link AI outputs back to their source documents.
Citation tracking is the difference between "the AI said so" and "the AI found this in your Q3 policy document, page 4." Without it, your AI is a black box. Information comes out, but nobody knows where it came from. With citation tracking, every answer carries a trail back to its sources.
Think about how your team handles internal questions today. Someone asks about a process. Another person answers from memory. But memory is unreliable. Citation tracking ensures that when AI answers a question, it shows exactly which documents it pulled from. Your team can click through, verify, and trust the answer because they can see the original.
Trust is not built on confidence. Trust is built on verifiability. When your team can click through to the source document, they trust the AI. When they cannot, every answer is suspect.
Citation tracking solves a universal problem: how do you know if the information you are acting on is accurate? Every business needs to trace decisions back to their sources.
Documents are chunked and indexed with metadata preserved. When AI retrieves chunks to answer a question, those metadata links travel with the answer. The user sees not just the response, but which specific documents contributed to it.
Ask a question and see the difference citations make. Without them, you are trusting blindly. With them, you can verify in seconds.
Source info travels with the content
When documents are chunked, each piece keeps a reference to its origin: document name, page number, section heading, last updated date. This metadata is stored alongside the vector embedding and retrieved together.
Sources appear in the response
The AI formats its response with citation markers. Each claim links to the specific chunk that supported it. Users see both the answer and its evidence in one view.
Full documents accessible alongside
A sidebar or expandable section shows the actual source documents. Users can read the original context, not just the chunk the AI selected. They can verify the AI did not misinterpret.
Your team member clicks the citation link. They see the exact paragraph from the procurement policy. The document was last updated two weeks ago. They confirm the threshold and proceed with confidence. Without citation tracking, they would either trust blindly or spend 20 minutes hunting through folders.
Hover over any component to see what it does and why it's neededTap any component to see what it does and why it's needed
Animated lines show direct connections · Hover for detailsTap for details · Click to learn more
You embedded all your documents beautifully. The AI retrieves relevant chunks. But you did not store which document each chunk came from. Now your AI says "based on your policies" with no way to verify. Your team learns to distrust it.
Instead: Store document ID, page/section, and timestamp with every chunk. Treat metadata as non-negotiable during ingestion.
The AI cites "HR Policy v2.3" but that document was replaced six months ago. Your team follows the cited source, finds old information, and loses confidence in the entire system.
Instead: Version your documents. Include versioning in citations. Alert when sources are superseded. Re-index when documents update.
Your AI says "Source: Operations Manual, Section 4.2" but there is no link. Users cannot click through. They would have to manually search, find the document, scroll to Section 4.2. Nobody does that. The citation becomes decoration.
Instead: Make every citation a direct link. One click to the exact location. If you cannot link, you do not really have citation tracking.
You have learned how to maintain the chain from AI output back to source documents. The natural next step is understanding how to balance keyword search with semantic search to find the right sources in the first place.