General Quality

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

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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?

databricks-solutions/ai-dev-kit/tree/main/.test/eval-criteria/general-quality commit 934e33cc49

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