Logic Templates
Appendix A — Configuration Reference
Logic templates are reusable transformation logic snippets that analysts can insert into notation tabs from the right-click context menu. They capture patterns that recur across mappings — common field transformations, standard exclusion language, default-value patterns — and make them available in one click rather than re-typed or copy-pasted.
Fields
Category — Groups templates together in the context menu. The category label becomes a submenu header. Templates are sorted alphabetically by category, then by Display Order within each category. New templates default to a "Custom" category.
Name — The label shown in the context menu when inserting the template. Must be unique within its category.
Content — The text inserted into the notation tab when the template is applied. The content can be anything — prose, pseudocode, SQL, or a structured pattern. Plain language pseudocode is recommended over SQL: the whole point of a logic template is to communicate the intended transformation to analysts and reviewers who may not be writing the loader themselves. SQL syntax tends to work against that goal. You can always refine into SQL later; a template that reads If source value is null, use 'UNKNOWN' is more useful to most readers than the equivalent CASE expression.
Default Value — If set, applying this template also populates the mapping's Default Value field with this literal. This is most meaningful for templates representing a specific hardcoded default — a conversion user identifier, a sentinel value, a known fallback. The editor does not restrict which templates can carry a Default Value, but setting it on templates where it has no logical meaning will overwrite whatever the analyst had in that field.
Usage Guidance — Optional notes on when and how to use this template. Not inserted into the mapping — visible in the editor only, as a reference for whoever maintains the configuration.
Notes
Logic templates are embedded into the AI modelfile at deployment time, giving the AI model knowledge of your standard transformation patterns. A well-maintained template library improves the quality of AI mapping suggestions. See Chapter 11.