Chunk Attribution
Understand how to measure and optimize the impact of retrieved chunks in your RAG pipeline
Chunk Attribution measures whether or not each chunk retrieved in a RAG pipeline had an effect on the model’s response.
Chunk Attribution is a binary metric: each chunk is either Attributed or Not Attributed.
A chunk is considered Attributed when:
- The model incorporated information from the chunk into its response
- The chunk influenced the model’s reasoning or conclusions
- The chunk provided context that shaped the response in some way
Chunks that are retrieved but have no discernible impact on the model’s output are marked as Not Attributed.
Understanding Attribution
Example Scenario
Consider this simple example that illustrates chunk attribution:
User query: “What are the health benefits of green tea?”
- Chunk 1: “Green tea contains antioxidants that may reduce the risk of heart disease.”
- Chunk 2: “Black tea is produced by oxidizing tea leaves after they are harvested.”
- Chunk 3: “Studies suggest green tea may help with weight loss and metabolism.”
Model response: “Green tea offers several health benefits, including antioxidants that may reduce heart disease risk and potential effects on weight loss and metabolism.”
Attribution analysis: Chunks 1 and 3 would be Attributed because information from them appears in the response. Chunk 2 would be Not Attributed because it contains information about black tea, which wasn’t included in the response.
Optimizing Your RAG Pipeline
Recommended Strategies
When analyzing Chunk Attribution in your RAG system, consider these key optimization strategies:
Tune retrieved chunk count: If many chunks are Not Attributed, reduce the number of chunks retrieved to improve efficiency without impacting quality.
Debug problematic responses: When responses are unsatisfactory, examine which chunks were attributed to identify the source of issues.
Improve retrieval quality: Use attribution data to refine your retrieval algorithms and embedding models.
Best Practices
Monitor Attribution Rates
Track the percentage of chunks that are attributed over time to identify trends and potential issues in your retrieval system.
Balance with Other Metrics
Use Chunk Attribution alongside Chunk Relevance and Chunk Utilization for a complete picture of retrieval effectiveness.
Optimize Chunk Size
Experiment with different chunk sizes to find the optimal balance between attribution rates and information density.
Improve Retrieval Quality
Use attribution data to refine your retrieval algorithms and embedding models.
When optimizing for Chunk Attribution, be careful not to reduce the number of chunks too aggressively, as this may limit the model’s access to potentially useful information in edge cases.