> ## 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 Relevance > Understand how to measure and optimize the relevance of retrieved chunks to user queries in your RAG pipeline export const DefinitionCard = ({children}) => { return

{children}

; }; export const Pill = ({label, color, backgroundColor}) => {label} ; Chunk Relevance measures whether a given chunk of text contains information that could help answer the user's query in a RAG pipeline. Chunk Relevance is a binary metric: each chunk is either or . A chunk is considered when: * The chunk contains at least some information that is useful to answer the query (even partially) * The chunk provides a key piece of information (such as the name of an entity or a specific fact) that can be used to find the answer in another chunk * Any part of the chunk helps answer the query either partially or completely A chunk is considered when: * The chunk contains no useful information for answering the query * The chunk is completely off-topic or only contains background/tangential information with no bearing on the query * The content is topically related but doesn't answer the question even partially We do not require the chunk to fully answer the query—even partial relevance is sufficient. We do not penalize for incompleteness; as long as something in the chunk is relevant, we label it as Relevant. ## Calculation method Chunk Relevance is computed through a multi-step process: Additional evaluation requests are sent to an LLM to analyze each chunk's relevance to the user query. Each chunk is evaluated independently against the query to determine its relevance, without using outside knowledge or making assumptions. Each chunk receives a binary classification: Relevant (true) if it provides any useful information, or Not Relevant (false) if it contains no useful information for the query. The evaluation produces both a classification for each chunk and an overall explanation of the reasoning. This metric is computed by prompting an LLM, and thus requires additional LLM calls to compute, which may impact usage and billing. ## Understanding chunk relevance

Example Scenario

This example illustrates chunk relevance:

User query: "What is the population of the capital city of France?"

Retrieved chunks:

Chunk 1: "The capital city of France is Paris."
Chunk 2: "Paris has a population of approximately 2.1 million people."
Chunk 3: "France is a country located in Western Europe."

Relevance analysis: Chunks 1 and 2 are because they provide information directly related to answering the query. Chunk 1 provides crucial context (the capital is Paris) that enables the answer to be found, and Chunk 2 provides the actual answer. Chunk 3 is because it only provides general background information about France's location, which doesn't help answer the population question.

Chunk Relevance forms the foundation for other retrieval metrics, including [Context Precision](/concepts/metrics/rag/retrieval-quality/context-precision) and [Precision @ K](/concepts/metrics/rag/retrieval-quality/precision-at-k). ### Chunk Relevance vs. Context Relevance * **Chunk Relevance** answers: "Is this specific chunk useful for this query?" Use it when you want to debug retrieval at the **chunk level** — pruning noisy chunks, tuning your chunking strategy, or checking which documents are actually helpful. * **Context Relevance** ([Context Relevance](/concepts/metrics/rag/retrieval-quality/context-relevance)) answers: "Taken together, does my retrieved context contain enough information to answer the query?" Use it when you want a **single signal for the whole retrieved context** — deciding whether to increase Top K, change your retriever, or trigger a fallback flow. In practice, start with **Context Relevance** to see if retrieval is working at all, then drill down with **Chunk Relevance** to understand which specific chunks are helping or hurting. ## Optimizing your RAG pipeline

Addressing Low Relevance Scores

When many chunks are marked as , it indicates issues with the retrieval system. To improve the system:

Improve retrieval quality: Refine embedding models, similarity search algorithms, or retrieval parameters to better match queries with relevant content.

Optimize chunking strategy: Ensure chunks are semantically coherent and contain complete information units rather than arbitrary text splits.

Adjust retrieval parameters: Experiment with different Top K values, similarity thresholds, or reranking strategies to improve relevance.

Analyze patterns: Identify common characteristics of non-relevant chunks to understand why the retrieval system is selecting them.

## Best practices Chunk Relevance works alongside [Context Precision](/concepts/metrics/rag/retrieval-quality/context-precision) and [Precision @ K](/concepts/metrics/rag/retrieval-quality/precision-at-k) for a comprehensive view of retrieval effectiveness. Relevance scores help refine embedding models, similarity search algorithms, and retrieval parameters. Tracking relevance rates across different query types helps identify patterns and improve retrieval system performance. Chunk Relevance is designed to be lenient—we mark chunks as relevant if they provide any useful information, even if incomplete. This ensures that chunks with partial answers or bridging information are not incorrectly marked as irrelevant.