AI app generation

Generation is iterative: the assistant plans structural changes, applies precise edits to your source files, validates syntax, and suggests next steps—so you steer the product without hand-merging diffs.

How a generation turn works

Behind the scenes the platform runs a pipeline similar to plan → locate files → patch → validate → verify. Outputs are applied as search-and-replace or scoped file updates rather than replacing the whole repo blindly.

Declarative payloads can also create or update record types, sample records, workflows, translations, and i18n config alongside code.
Streaming status updates keep the UI informed during long runs.
If validation fails, you get actionable errors without silently broken bundles.

Chat-first workflow

Treat the assistant as a pair programmer: give goals, constraints, and examples. Follow up with smaller requests after each successful apply—this conserves credits and reduces risk compared to one giant prompt.

Name components, user flows, and data entities explicitly (“Lead capture form posting to RecordType leads”).

ai-generation-chat-flow

Placeholder: prompt → streaming → preview update → suggested next steps.

What the AI can generate

Besides UI, the assistant can scaffold backend-facing configuration that the platform understands: CRUD-oriented record types, workflow graphs, translation dictionaries, and analytics-friendly structure.

Credits are consumed per operation; typical flat rates are listed under Subscriptions & credits, with dynamic pricing possible for heavy model usage.

Quality and recovery

If a turn does not successfully apply edits, billing may be reduced (see credit policy). Use source version history to roll back to a known-good snapshot, then retry with a narrower instruction.