Document dataset columns without guessing their meaning

Create a column-level data dictionary that separates documented definitions, observed properties and unresolved semantics.

PLPromptByte Library@promptbytePublished Sep 8

TextData & Analysis #data dictionary #data quality #metadata

Version 1 of 1 · published Sep 8, 2026Version 1 · only version

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Dataset context{{dataset_context}}

Purpose, row grain and known provenance. Default: An order export used for operations reporting; intended grain is one row per order line.

Schema samples{{schema_samples}}

Column definitions, representative rows and authoritative metadata. Default: Columns: order_id, line_id, amount, currency, created_at, status. Sample: O101,L1,19.99,USD,2026-08-01T10:00:00Z,2. Notes: amount excludes tax; status-code meanings and key constraints are not documented.

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Create a column-level data dictionary.

Dataset purpose and row grain: An order export used for operations reporting; intended grain is one row per order line.
Schema, sample rows and existing notes: Columns: order_id, line_id, amount, currency, created_at, status. Sample: O101,L1,19.99,USD,2026-08-01T10:00:00Z,2. Notes: amount excludes tax; status-code meanings and key constraints are not documented.

For each column, record name, documented meaning, declared type, observed format, unit, allowed values, missing-value convention, key/relationship role and source note. Mark each property as supplied, observed in the sample, or unresolved. A suggestive column name is not proof of its meaning.

Check for ambiguous identifiers, mixed units, timestamp conventions, coded values and fields that might be derived. List precise questions for the data owner and proposed validation checks.

Keep sample observations separate from whole-dataset claims: a small sample cannot prove uniqueness, completeness or valid ranges. End with a short usage note describing the grain and joins that are established by the evidence.

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Usage notes

Share source-system definitions when available. Treat inferred field meanings as questions until a data owner confirms them.

Adapted by PromptByte from prompts/development/database-schema-designer.md by AJ Geddes, under MIT. Adapted Database Schema Designer's column, type, key and relationship documentation into a descriptive data dictionary. Added evidence labels to avoid inventing semantics or sample-wide guarantees. No model run or example result is claimed.

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MIT License

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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

code · sql
```
**Assumptions**

- `order_items.order_id` joins to `orders.id`, and `products.category_id` joins to `categories.id`. These are inferred from the naming convention, since no foreign keys were listed.
- "Last 90 days" is measured against `orders.order_date` relative to today, inclusive of today.
- Revenue is `quantity * unit_price` summed over line items; there is no discount or tax column in the schema, so none is applied.
- Cancelled orders are excluded. If `status` uses different values than `'
```
Code preview: **Assumptions** - `order_items.order_id` joins to `orders.id`, and `products.category_id` joins to `categories.id`. These are inferred from the naming convention, since no foreign keys were listed. - "Last 90 days" is measured against `orders.order_date` relative to today, inclusive of today. - Revenue is `quantity * unit_price` summed over line items; there is no discount or tax column in the schema, so none is applied. - Cancelled orders are excluded. If `status` uses different values than `'
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Document dataset columns without guessing their meaning

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SQL query generator from a plain-English request

Audit SQL joins for duplicate rows and inflated totals

Design a forecast backtest that respects time and information

Critique an existing chart for misleading interpretation

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Discussion 0