> ## 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.
# Completeness
> Measure how thoroughly your model's response covers the relevant information available in the provided context
export const DefinitionCard = ({children}) => {
return
{children}
;
};
Completeness measures how thoroughly your model's response covered the relevant information available in the context provided.
Completeness and Context Adherence are closely related, and designed to complement one another:
* **Context Adherence** answers the question, "is the model's response consistent with the information in the context?"
* **Completeness** answers the question, "is the relevant information in the context fully reflected in the model's response?"
In other words, if Context Adherence is "precision," then Completeness is "recall."
## Understanding completeness
Consider this simple, stylized example that illustrates the distinction:
User query: "Who was Galileo Galilei?"
Context: "Galileo Galilei was an Italian astronomer."
Model response: "Galileo Galilei was Italian."
Analysis: This response would receive a perfect Context Adherence score: everything the model said is supported by the context. But this is not an ideal response. The context also specified that Galileo was an astronomer,
and the user probably wants to know that information as well. Hence, this response would receive a low Completeness score.
Tracking Completeness alongside Context Adherence allows you to detect cases like this one, where the model is "too reticent" and fails to mention relevant information.
## Improving low completeness
To fix low Completeness values, we recommend:
Adjust your prompt: Tell the model to include all the relevant information it can find in the provided context.
Refine retrieval: Ensure your retrieval system is bringing back the most relevant context chunks.
Balance with conciseness: While improving completeness, maintain appropriate response length for your use case.
## Best practices
Track completeness scores across different types of content and queries to identify patterns where information is consistently omitted.
Find the right balance between completeness and keeping responses concise and focused for your specific use case.
Use completeness alongside context adherence, correctness, and relevance metrics for a comprehensive view of response quality.
Evaluate completeness with complex, multi-part queries that require synthesizing information from multiple context sections.
When optimizing for completeness, be careful not to encourage your model to include irrelevant information just to achieve higher scores. The goal is to include all relevant information while maintaining focus on the user's query.