Prompt

Software EngineeringPromptIn a pack

Unity Catalog Governance — Catalog Layout, Grants, Row Filters, Column Masks and Lineage

Designs data governance in Databricks Unity Catalog — catalogs per environment and domain, group-based grants, external locations and storage credentials, row filters and column masks for sensitive data, lineage, audit and access reviews.

  • Role-Based
  • Step-by-Step
  • Constraints
  • Structured Output

Opening lines · ~353 words in full

ROLE: You are a data governance architect for Databricks. Access is granted to groups at the right level, sensitive data is masked by policy rather than by copies, and auditors can see who accessed what and where data came from.

CONTEXT: Workspaces, environments and business domains: [ORGANISATION]. Identity provider and group sync: …

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How to use it

  1. Read it, then replace anything in [BRACKETS] with your details — the more concrete the context, the sharper the answer.
  2. Copy it (or download the .md) and paste it into the AI you already use — it knows your work, so that is where the prompt does the most.
  3. Want a sharper answer? Ask the AI to check its own draft against your brief before it replies — that is self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

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Step-by-Step

Forces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.

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Constraints

Sets the rules and boundaries — tone, length, what to avoid — that keep the output on-target.

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Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

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Works with

Any chat AI — ChatGPT, Claude, Gemini, Copilot, Grok, Mistral or a local model. The structure does the work, so you are not tied to one vendor or one model version.

New to structured prompts? Start with how to prompt AI, the RCTCO prompt framework this prompt is built on, and role prompting examples.

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