Traceability and Citations

The single biggest difference between a chat-bot and a RAG system is Traceability. Users need to know exactly where the information came from to trust it.

The Citation Pipeline

1. Tagging Chunks: Every chunk in your context must have a reference ID (e.g., DOC-01, MODULE-A).
2. Forced Citation Prompt: Instruct the LLM to cite its sources using a specific format.
3. Link Reconstruction: Convert these tags back into clickable URLs or file paths in the UI.

Prompting for Citations

Use the provided context to answer the user query.
For every factual claim, cite the document ID in brackets, e.g., [DOC-1].
If you cannot find the answer in the context, say you don't know.

Multimodal Citations

Citing an image or video is more complex:

• Image: "As shown in the diagram [IMG-4]..."
• Video: "The presenter mentions this at 04:22 [VID-1]..."

Metadata Mapping

To make citations useful, your metadata must be rich.

Citation ID	Source File	Page	Deep Link
[1]	fiscal_report_2024.pdf	14	mysite.com/doc/123#page=14

Handling Hallucinated Citations

Sometimes LLMs "invent" citations (e.g., citing [DOC-5] when only 4 docs were provided). Prevention:

• Use a verification step to cross-reference the model's citations against the provided input list.
• Use structured output (JSON) to force the model to separate its "Answer" from its "Sources".

Exercises

1. Design a JSON schema for a RAG response that includes an array of citations.
2. Why is citing the "Author" and "Publication Date" important for legal RAG?
3. How would you handle a citation for a 2-hour long video?