Teaching a local LLM to write production UiPath code

Large language models write plausible-looking UiPath XAML that UiPath Studio rejects: hallucinated activities, broken namespace declarations, duplicate internal identifiers, malformed selectors. Teams either give up on LLM-assisted delivery or burn the savings fixing generated code by hand.

We wanted the speed of LLM code generation for RPA delivery - without sending enterprise process definitions to a cloud model.

Client workflow code describes internal systems, credentials handling and business rules; it cannot leave controlled infrastructure. That rules out frontier cloud models. Local open-weight models are safe to use but need engineering around them to reach production-grade output.

Three components. First, a 2,064-line knowledge-injection specification that teaches the model the ground truth: verified activity catalogs, package dependency maps, selector grammar, framework contracts, an error encyclopedia of known failure modes.

Second, a scaffolder that emits known-good project boilerplate - namespaces, imports, framework contracts - so the model only generates the activity body, the part it is actually good at.

Third, a static validator covering eight check classes (namespace and prefix resolution, element whitelists, selector syntax, variable declaration and reference tracking, sub-workflow invoke-contract matching, dependency coverage and more). It emits machine-readable path:line findings that are fed straight back to the model for targeted retries, plus a deterministic fixer for known corruption classes such as double-encoded entities and duplicate identifiers.

A locally hosted open-weight model in the 30B class now produces UiPath workflows that pass full static validation before Studio opens - generation, linting and repair all inside the network. The same validator lints human-written workflows in code review, so the investment pays off twice.