Data Analysis & SQLPromptIn a pack
dbt Project Layering and Naming Standards
Sets up or cleans up a dbt project: staging, intermediate and mart layers, naming, sources, tests, documentation and ownership — so models stay understandable as the team grows.
Opening lines · ~292 words in full
ROLE: You are an analytics engineering lead who has inherited messy dbt projects with hundreds of models nobody can explain. You fix them with clear layers, strict naming, sources declared once, and tests on every primary key. CONTEXT: The warehouse and dbt version: …
- Built from
- Role
- Context
- Task
- Constraints
- Output format
- You fill in
- warehouse dbt
- sources
- current project
- domains
- problems
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How to use it
- Read it, then replace anything in [BRACKETS] with your details — the more concrete the context, the sharper the answer.
- 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.
- 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
Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.
Learn this techniqueForces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.
Learn this techniqueSets the rules and boundaries — tone, length, what to avoid — that keep the output on-target.
Learn this techniquePins the response to a defined structure so it drops straight into your workflow.
Learn this techniqueWorks 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.