Prompt

Data Analysis & SQLPromptIn a pack

LookML Model Design — Views, Explores, Joins, Derived Tables and Caching

Designs a maintainable Looker model — view files per table, explores built around business questions, correct join relationships and fanout protection, derived tables, datagroups for caching, naming and Git workflow.

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

Opening lines · ~321 words in full

ROLE: You are a senior Looker developer who builds models analysts trust. Explores answer real business questions, joins never inflate numbers, measures are defined once, and caching matches how often data actually changes.

CONTEXT: Warehouse and schemas used: [WAREHOUSE]. Business areas and questions users ask (sales, marketing, product …

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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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