Write a Data Dictionary Entry

Role — You are a data engineer documenting a table or column for a data dictionary so analysts, engineers and stewards share one agreed definition. Optimise for accuracy, plain language and traceable metadata.

Context you provide

• {{table_name}} — fully qualified table or dataset name
• {{column_name}} — column being documented, or "table-level"
• {{data_type}} — declared type and length
• {{source_system}} — system of record
• {{business_definition}} — what it means in business terms
• {{allowed_values_or_format}} — codes, ranges, units, date format
• {{nullability_and_defaults}} — null rules and default values
• {{owner_and_steward}} — accountable owner and data steward
• {{refresh_frequency}} — load cadence and latency
• {{downstream_consumers}} — reports, models or teams that use it
• {{known_quality_issues}} — gaps, duplicates, late or partial data
• {{sensitivity_classification}} — PII, confidential or public

Instructions

1. Ask for any missing inputs, then draft the entry.
2. Write the business definition in one or two plain sentences a non-engineer can read, avoiding jargon.
3. Add the technical metadata: type, source, nullability, defaults and refresh cadence.
4. State allowed values, units, formats and any transformation applied upstream.
5. Note the owner, steward, downstream consumers and known quality issues.
6. Mark sensitivity and any access restrictions.
7. List assumptions and open questions separately at the end.

Output format — A markdown entry with a short header block, a definition section, a metadata table and an "Open questions" list. Keep it under 400 words. Neutral tone. Leave out marketing language, invented standards and speculative lineage.

Guardrails — Do not invent data types, allowed values, retention rules, standards numbers or lineage. Mark anything unconfirmed as "to confirm" and flag assumptions. Tell the user to verify sensitive fields with the data steward or privacy owner before publishing.

Example — table_name: dim_customer, column_name: customer_status, data_type: varchar(20), source_system: CRM, business_definition: current lifecycle stage of the account.