If you've built a RAG (Retrieval-Augmented Generation) system that pulls information from multiple documents, you've probably hit this frustrating wall: your AI agent retrieves the right information but can't tell you which document it came from, or worse, it confidently cites the wrong source.
I recently worked with a client facing exactly this problem. They built an agent to extract data from hundreds of internal documents, but the system kept mixing up sources and couldn't reliably attribute information to specific files. The issue wasn't their RAG architecture—it was how they prepared and labeled their data before vectorization.
In this article, I'll walk you through the practical steps to properly label data for RAG systems, ensuring your agents can accurately track sources and understand document context. This isn't theoretical advice—these are the specific techniques we used to fix the attribution problem.
Why RAG Systems Lose Track of Document Sources
Before jumping into solutions, let's understand why this happens. When you vectorize documents for RAG, you're converting text into numerical representations (embeddings) that live in a vector database. Here's where companies typically go wrong:
The common mistake: They chunk documents into segments, vectorize those chunks, and store them with minimal metadata. When the retrieval system finds relevant chunks, it has no way to properly identify which document they came from or how they relate to each other.
What actually happens: Your vector database returns semantically similar text chunks, but without proper labeling, the LLM can't determine if two chunks came from the same document, what section they belonged to, or which specific file contained them. It's like tearing pages out of different books, mixing them up, and expecting someone to reassemble them correctly.
The solution isn't more sophisticated retrieval algorithms—it's better data labeling before vectorization.
Essential Metadata Fields for RAG Data Labeling
Proper data labeling for RAG starts with comprehensive metadata attached to every chunk. Here's the metadata structure that solved the source attribution problem for my client. Remember that embedding quality matters more than your vector database choice, so focus on data preparation first:
source_file_name
The exact filename or document ID
source_file_path
Full path if working with folder structures
document_type
Contract, report, email, invoice, etc.
document_date
Creation or modification date
document_author
If relevant for attribution
document_title
Human-readable title
chunk_id
Unique identifier for each text segment
chunk_sequence
Position within the original document (e.g., chunk 3 of 47)
section_title
The heading or section this chunk belongs to
page_number
If applicable for PDFs or paginated documents
parent_chunk_id
Links to previous chunk for context
chunk_type
Body text, table, list, header, etc.
Data Preprocessing: Structuring Documents Before Vectorization
How you chunk and structure documents before vectorization directly impacts source attribution accuracy. Here's the preprocessing approach that works:
Semantic chunking over arbitrary splits
Most RAG implementations use fixed token counts (512, 1024 tokens) to chunk documents. This breaks paragraphs mid-sentence and separates related information. Instead, chunk by semantic boundaries—paragraphs, sections, or logical breaks. This keeps context intact and makes source attribution clearer.
Preserve document hierarchy
Maintain the structural relationships in your documents. If you're processing a report with sections and subsections, your metadata should reflect this hierarchy. For example: section_level_1: 'Financial Analysis', section_level_2: 'Quarterly Revenue Breakdown', section_level_3: 'Q3 Regional Performance'. When your agent retrieves a chunk about Q3 revenue, it can tell the user exactly where in the document structure that information lives.
Overlap strategy for context
Use overlapping chunks (typically 10-20% overlap) to ensure context isn't lost at chunk boundaries. But critically, label these overlapping sections so your system knows they're connected. Store the relationship: 'This chunk overlaps with chunk_id_245 by 50 tokens.'
Handle multi-modal content
If your documents contain tables, charts, or images, label these distinctly. For tables, consider storing them as structured data alongside a text description. Tag them with content_type: table so your retrieval system can handle them appropriately.
Vectorization Strategy for Multi-Document RAG
The vectorization process itself needs to account for your labeling strategy. Here's what worked for maintaining source accuracy. If you're still deciding on models, check our guide on which embedding model to use for RAG:
Embed metadata alongside content
Don't just vectorize the raw text—include key metadata in the embedding process. For example, prepend each chunk with context like: '[Document: Q3_Financial_Report.pdf | Section: Revenue Analysis | Page: 12]' before vectorization. This embeds source information directly into the vector representation.
Create document-level embeddings
In addition to chunk embeddings, create a single embedding for each complete document (using the title, summary, or first few paragraphs). This allows two-stage retrieval: first identify relevant documents, then retrieve specific chunks from those documents. It dramatically improves source attribution.
Namespace your vector database
If your vector database supports namespaces or collections (like Pinecone or Weaviate), separate documents into logical groupings. Store all chunks from contracts in one namespace, all chunks from reports in another. This gives your retrieval system another dimension for accurate sourcing.
Version control for document updates
If documents get updated, don't just overwrite old vectors. Append version metadata: document_version: 2.1 and last_updated: 2025-09-15. This prevents confusion when information changes across document versions.
Implementing Source Tracking in Your RAG Pipeline
With properly labeled data, your RAG pipeline needs to use that metadata effectively. Here's the implementation pattern:
During retrieval
When your system queries the vector database, retrieve not just the text chunks but all associated metadata. Most vector databases return metadata alongside vectors—ensure your retrieval function captures this.
In the prompt context
When passing retrieved chunks to your LLM, include source metadata explicitly in the prompt. Format it clearly: Context from [Q3_Financial_Report.pdf, Section: Revenue Analysis, Page: 12]: 'Revenue increased 23% year-over-year driven by enterprise contracts...' This explicit sourcing in the prompt helps the LLM maintain source awareness when generating responses.
Response formatting
Instruct your LLM to cite sources in its responses. In your system prompt, require it to reference documents like academic citations. For example: 'According to the Q3 Financial Report (page 12), revenue increased 23%...'
Common Data Labeling Mistakes That Break RAG Attribution
Through multiple RAG implementations, I've seen these mistakes repeatedly cause source attribution failures. If traditional RAG isn't working, consider exploring alternatives like CAG and GraphRAG:
Insufficient metadata granularity
Labeling chunks with just a filename isn't enough. You need section, page, and sequence information to accurately attribute sources, especially in long documents.
Inconsistent naming conventions
If your document names or metadata fields aren't standardized, your RAG system can't reliably group or attribute information. Establish naming standards before you start labeling.
Ignoring document relationships
Many RAG systems treat all documents as isolated entities. In reality, documents often reference each other. If you have linked documents (like a contract and its amendments), label these relationships explicitly.
Static metadata
Your labeling system should account for changing documents. Version control and update timestamps aren't optional—they're essential for maintaining accuracy over time.
Overlooking chunk context
Storing chunks without information about surrounding chunks creates 'contextual islands.' Your agent can't understand that two retrieved chunks are from consecutive paragraphs unless you label that relationship.
Testing and Validating Source Attribution
After implementing proper data labeling, you need to verify it works. Here's my testing approach:
Create a source attribution test set
Build 20-30 test queries where you know exactly which documents contain the answer. Query your RAG system and check if it correctly identifies the source documents.
Measure attribution accuracy
Track what percentage of responses correctly cite sources. We aim for 95%+ accuracy on source attribution. If you're below 90%, your labeling strategy needs refinement.
Test edge cases
Query for information that appears in multiple documents with slight variations. Can your system distinguish between sources? Can it identify when information is contradictory across documents?
User acceptance testing
Have actual users interact with the system and flag attribution errors. Users quickly notice when sources don't match the information provided.
Practical Implementation: A Step-by-Step Workflow
Here's the exact workflow we implemented for the client with source attribution problems:
Document ingestion audit
Review all source documents and establish a metadata schema that captures necessary source information. When handling sensitive data, ensure you follow best practices for preventing data leakage in AI applications.
Preprocessing pipeline
Build a preprocessing script that extracts text, preserves structure, and applies semantic chunking with 15% overlap.
Metadata enrichment
For each chunk, programmatically generate all required metadata fields (document info, chunk sequence, section hierarchy, etc.).
Vectorization with context
Prepend metadata context to chunks before embedding, and store complete metadata alongside vectors in the database.
Retrieval enhancement
Modify the retrieval function to return metadata with each chunk and rank results considering both semantic similarity and document relevance.
Prompt engineering
Update system prompts to require source citations and format retrieved context with explicit source attribution.
Validation loop
Run attribution tests, identify failures, and refine metadata labeling where errors occur.
Building Reliable Source Attribution
RAG systems fail at source attribution because of inadequate data labeling, not flawed retrieval algorithms. By implementing comprehensive metadata schemas, semantic chunking strategies, and proper vectorization approaches, you can build RAG systems that reliably track information back to source documents.
The key insight: treat data labeling as a first-class concern in your RAG pipeline, not an afterthought. The time invested in proper labeling upfront saves countless hours debugging attribution errors later.
If you're building multi-document RAG systems, start by auditing your current metadata strategy. Are you capturing document hierarchy, chunk relationships, and version information? If not, that's where source attribution problems originate.