Rag Scenarios And Solutions
Incorrect Citation Format
AI provides sources in inconsistent formats, with wrong URLs, missing page numbers, or citations that don't match retrieved content.
TL;DR
AI provides sources in inconsistent formats, with wrong URLs, missing page numbers, or citations that don't match retrieved content.
Key Takeaways
- The Problem
- Deep Technical Analysis
- How to Solve
- Agent Instructions: Querying This Documentation
The Problem
AI provides sources in inconsistent formats, with wrong URLs, missing page numbers, or citations that don't match retrieved content.
Symptoms
- ❌ Citations point to wrong documents
- ❌ "Source: Unknown" despite using context
- ❌ Inconsistent citation style
- ❌ Made-up source references
- ❌ Cannot verify claims
Real-World Example
AI response: "The rate limit is 1000/hour [Source: API Documentation, page 15]"
Problems:
→ Document has no page numbers (web page)
→ Claim is from chunk_id: api_v2_limits_003
→ Citation format doesn't match actual source structure
User cannot verify the claim
Deep Technical Analysis
Citation Generation Methods
Inline Metadata:
Pass source info in context:
"[doc_id:api_guide_v2, title:'API Limits', url:'/docs/api#limits']
The rate limit is 1000 requests per hour."
LLM must extract and format:
→ Often loses metadata
→ Or formats inconsistently
Post-Processing:
Extract citations after generation:
1. Identify factual claims in response
2. Match to source chunks
3. Append citations
More reliable but complex
How to Solve
Implement structured citation format in system prompt + pass chunk IDs inline with content + use post-processing to verify citations + provide citation template ("According to [doc]...") + include direct URLs in metadata. See Citation Management.
Agent Instructions: Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.
Perform an HTTP GET request on the current page URL with the ask query parameter:
GET /dev/rag-scenarios-and-solutions/llm/citation-format.md?ask=<question>
The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.
Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.
Related Pages
Last updated January 26, 2026


