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Completeness measures the degree to which an AI-generated response fully addresses every necessary aspect of the user’s query, including subcomponents, with sufficient detail, relevance, factual accuracy, and logical structure.
Completeness is returned as a continuous score ranging from 0 to 1:
01
Low Completeness
The response is incomplete or misses key parts of the query
High Completeness
The response is thorough and addresses all aspects of the query
A higher score indicates a more thorough, precise, and well-organized response. This metric helps ensure that users receive responses that are comprehensive and well-reasoned.

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Understanding Completeness

The Completeness metric evaluates each model response along four key dimensions:
  • Coverage: Does the response address all parts of the user’s query?
  • Detail and Depth: Does it go beyond surface-level answers, providing sufficient elaboration?
  • Relevance: Is the content strictly pertinent to the query, without unnecessary digressions?
  • Logical Coherence: Is the response clearly organized and logically structured?

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Example Scenario

Completeness detects when a response may be technically correct but fails to satisfy the full intent of the user’s query.
User query: “Describe how a bill becomes a law in the U.S., and give an example of a recent bill.”
Model response: “A bill becomes a law after being approved by Congress and signed by the President.”
Analysis: The response is factually correct and coherent, but fails to mention an example and lacks procedural detail. It would score poorly on Coverage and Detail, lowering the overall Completeness score.

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Improving Low Completeness

To improve low completeness scores:
Use prompt structure effectively: Encourage models to break down multi-part questions and address each subcomponent clearly.
Guide for elaboration: Add instructions to provide examples, reasoning, or structured breakdowns to increase depth.
Balance brevity and thoroughness: Ensure the model delivers full coverage without overwhelming verbosity.

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Best Practices

Evaluate Multi-Part Queries

Run completeness evaluations on questions that involve multiple steps or clauses to surface coverage gaps.

Combine With Correctness

Completeness doesn’t always imply correctness—an answer isn’t complete if it’s factually wrong. Use both metrics together.

Track Dimension-Level Gaps

Monitor which specific dimensions (e.g., Coverage, Detail, Relevance) consistently lead to low scores, so you can target improvements more precisely.

Customize to User Intent

Tailor completeness thresholds based on how critical thoroughness is in your domain (e.g., legal vs. creative writing).
A complete answer is not just one that says a lot—but one that says everything that’s necessary, says it well, and says only that. DeepRails helps ensure your AI systems meet that bar.
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