> ## Documentation Index > Fetch the complete documentation index at: https://docs.galileo.ai/llms.txt > Use this file to discover all available pages before exploring further. # Chunk Attribution Utilization > Understand how to measure and optimize the impact of retrieved chunks in your RAG pipeline export const Scale = ({low, mid, high, lowLabel = "Low", midLabel = "Mid", highLabel = "High", lowDescription, midDescription, highDescription, midColor = "yellow", inverted = false}) => { const lowColor = inverted ? "green" : "red"; const highColor = inverted ? "red" : "green"; const gradientId = inverted ? "greenToRed" : "redToGreen"; return

{low}

{mid &&

{mid}

}

{high}

{lowLabel}

{lowDescription &&

{lowDescription}

}
{mid &&

{midLabel}

{midDescription &&

{midDescription}

}
}

{highLabel}

{highDescription &&

{highDescription}

}
; }; export const DefinitionCard = ({children}) => { return
{children}
; }; export const Pill = ({label, color, backgroundColor}) => {label} ; Chunk Attribution Utilization is a dual metric that calculates two values - [chunk attribution](#chunk-attribution) and [chunk utilization](#chunk-utilization). ## Chunk attribution 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 or . A chunk is considered 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 . ### Understanding attribution

Example Scenario

Consider this simple example that illustrates chunk attribution:
User query: "What are the health benefits of green tea?"
Retrieved chunks:
  • 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 because information from them appears in the response. Chunk 2 would be because it contains information about black tea, which wasn't included in the response.
### Optimizing your RAG pipeline for chunk attribution

Recommended Strategies

When analyzing Chunk Attribution in your RAG system, consider these key optimization strategies:
Tune retrieved chunk count: If many chunks are , 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.
## Chunk utilization Chunk Utilization measures the fraction of text in each retrieved chunk that had an impact on the model's response in a RAG pipeline. Chunk Utilization is a continuous metric ranging from 0 to 1: A chunk with low utilization contains "extraneous" text that did not affect the final response, indicating potential inefficiencies in your chunking strategy. Chunk Utilization is closely related to [Chunk Attribution](/concepts/metrics/rag/generation-quality/chunk-attribution): Attribution measures whether or not a chunk affected the response, while Utilization measures how much of the chunk text was involved in the effect. Only chunks that were Attributed can have Utilization scores greater than zero. ### Calculation method We use a fine-tuned in-house Galileo evaluation model based on a transformer encoder architecture. The same model computes Chunk Adherence, Chunk Completeness, Chunk Attribution and Utilization in a single inference call. For each token in the provided context, the model outputs a utilization probability indicating if that token affected the response. Chunk Utilization is computed as the fraction of tokens with high utilization probability out of all tokens in the chunk. The model is trained on carefully curated RAG datasets and optimized to closely align with the RAG Plus metrics. ### Optimizing your RAG pipeline for chunk utilization

Addressing Low Utilization Scores

Low Chunk Utilization scores could indicate one of two scenarios:
Oversized Chunks: Your chunks are longer than they need to be
  • Check if [chunk relevance](/concepts/metrics/rag/retrieval-quality/chunk-relevance) is also low, which confirms this scenario
  • Solution: Tune your retriever to return shorter, more focused chunks
  • Benefits: Improved system efficiency, lower cost, and reduced latency
Ineffective LLM Utilization: The LLM generator model is failing to incorporate all relevant information
  • Check if Chunk Relevance is high, which confirms this scenario
  • Solution: Explore a different LLM that may leverage the relevant information more effectively
  • Benefits: Better response quality and more efficient use of retrieved information
 ### Best practices Track the percentage of chunks that are attributed over time to identify trends and potential issues in your retrieval system. Use Chunk Attribution Utilization alongside [Chunk Relevance](/concepts/metrics/rag/retrieval-quality/chunk-relevance) for a complete picture of retrieval effectiveness. Experiment with different chunk sizes to find the optimal balance between attribution rates and information density. Use attribution data to refine your retrieval algorithms and embedding models. Compare Chunk Utilization scores across different LLMs to identify which models most efficiently use retrieved information. Look for patterns in low-utilization chunks to identify specific content types or formats that your system processes inefficiently. When optimizing for Chunk Attribution Utilization, 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.