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general-quality

General response quality evaluation. Always applicable regardless of domain. Covers response structure, actionability, clarity, and hallucination detection.

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databricks-solutions/ai-dev-kit
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March 16, 2026 at 17:21
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name
general-quality
description
General response quality evaluation. Always applicable regardless of domain. Covers response structure, actionability, clarity, and hallucination detection.
metadata
{"category":"evaluation","version":"1.0","applies_to":[]}
## General Quality Rubric When evaluating any Claude Code agent trace, check these dimensions: ### 1. Actionable Output - Can the user directly use the response (copy-paste code, follow steps)? - Are code examples complete and runnable, not fragments? - Are file paths, table names, and API references concrete? ### 2. Structured Response - Code is in fenced code blocks with correct language tags - Steps are numbered or bulleted for multi-step tasks - Explanations are concise — answer first, context after ### 3. No Hallucination - All referenced APIs, functions, and parameters must actually exist - Databricks features must be current (not deprecated) - Tool names in traces must match real MCP tool names - No invented catalog/schema/table names unless the user specified them ### 4. Conciseness - Answers the question without excessive preamble or disclaimers - Doesn't repeat the question back - Doesn't explain basic concepts the user didn't ask about ### 5. Error Handling - If the agent encountered errors during execution, did it recover? - Did it explain errors to the user when relevant? - Did it avoid silently swallowing failures?
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