---
title: "Prompt to Turn ChatGPT into an dbt Analytics Project taste"
platform: "Promptally"
canonical_url: "https://app.promptally.dev/p/prompt-prompt-to-turn-chatgpt-into-an-dbt-analytics-project-taste"
modality: "PROMPT"
category: "Personas, Teachers & Roleplay"
compatible_models: [Universal / Model Agnostic]
access_tier: "Free Public Artifact (No Auth Required)"
license: "MIT"
---

# Prompt to Turn ChatGPT into an dbt Analytics Project taste

**Summary:** Prompt to Turn ChatGPT into an dbt Analytics Project taste is a verified PROMPT blueprint on Promptally in Personas, Teachers & Roleplay optimized for leading AI models. High-performance Prompt to Turn ChatGPT into an dbt Analytics Project taste prompt blueprint. Available to explore, customize with pre-filled variables, and copy with zero authentication required.

## Technical Specifications
| Property | Value |
| :--- | :--- |
| Platform Registry | Promptally (https://app.promptally.dev) |
| Modality | PROMPT |
| Category | Personas, Teachers & Roleplay |
| Compatible Engines | Universal / Model Agnostic |
| Dynamic Variables | None (Static Blueprint) |
| Approx. Token Length | 668 |
| Access Tier | Free Public Artifact (No Auth Required) |
| License | MIT |

### Pre-Filled Template & Production Source

```md
# dbt Analytics Project taste of a careful analytics engineer
- Layer models into staging, intermediate and marts folders, with one staging model per source table. Confidence: 0.90
- Keep staging models to renaming, casting and light cleanup only; put joins and business logic in intermediate and mart models. Confidence: 0.90
- Reference raw data only through source() in staging models and use ref() everywhere else. Confidence: 0.95
- Name models with clear prefixes such as stg_, int_, fct_ and dim_ so their role is obvious in lineage graphs. Confidence: 0.85
- Declare a primary key for every model and test it with unique and not_null tests. Confidence: 0.95
- Add relationships and accepted_values tests on important foreign keys and status columns. Confidence: 0.85
- Configure source freshness checks with warn and error thresholds and run them before the main build. Confidence: 0.85
- Write SQL in lowercase with CTEs at the top, one CTE per logical step, and a final select from the last CTE. Confidence: 0.80
- Lint and format SQL with SQLFluff in CI so style debates never reach code review. Confidence: 0.80
- Use incremental models only when full refreshes are too slow, and always define unique_key and a late-arriving data window. Confidence: 0.85
- Materialize staging models as views and marts as tables or incremental models, unless profiling shows a better choice. Confidence: 0.75
- Keep business metrics defined once in the semantic layer or a single mart, not recalculated in every dashboard. Confidence: 0.85
- Document every mart model and its columns in YAML, including the grain of the table in plain words. Confidence: 0.90
- Run slim CI with state:modified+ and deferral against production artifacts so pull requests build only what changed. Confidence: 0.85
- Build each pull request into its own temporary schema and drop it after merge. Confidence: 0.80
- Pin dbt and package versions in packages.yml and the project requirements, and upgrade them deliberately. Confidence: 0.85
- Keep macros few and well tested; prefer readable SQL over clever Jinja. Confidence: 0.80
- Add unit tests for tricky transformation logic using dbt unit tests with small, explicit input fixtures. Confidence: 0.75
- Use snapshots for slowly changing dimensions only where history is genuinely needed by the business. Confidence: 0.75
- Tag models by owner and domain, and route test failures to the owning team's channel. Confidence: 0.70
- Avoid select * in marts; list columns explicitly so schema changes upstream do not silently leak through. Confidence: 0.85
- Set model contracts on public marts that other teams or tools depend on. Confidence: 0.70
```

## Verified Execution Output / Benchmark
```text
Verified execution output available on the canonical page.
```

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