Postpone features with uncertain user value, unresolved requirements, or expensive dependencies until the prototype proves its main task. Do not treat every AI build interaction as an identical unit of credit consumption.
Separate scope from usage
Lovable documents one credit balance spanning building and chatting, hosting the app and built-in backend, and deployed-app AI features. Actual use depends on the activity; a count of prompts is therefore not a complete usage estimate.
As practical editorial advice, maintain two notes: what the next build must establish, and which ongoing activities the resulting app could introduce. A feature that takes little discussion may still add hosting or AI-feature usage after deployment. The supplied evidence does not establish a per-feature cost formula.
Fill in an uncertainty-versus-value worksheet
For each proposed feature, record:
- User outcome: the specific task it helps someone complete.
- Value score: 0 for decorative, 1 for helpful, 2 for necessary to the main task.
- Uncertainty score: 0 for agreed behaviour, 1 for one open question, 2 for several unresolved assumptions.
- Dependency: another feature, data source, or external decision required first.
- Evidence needed: one observation that would justify building it.
- Disposition: build now, clarify first, or postpone.
These scores are an editorial prioritisation aid, not a platform budgeting model. A high-value, high-uncertainty feature usually needs a smaller experiment before implementation, rather than automatic inclusion or rejection.
Hypothetical scope cut for a tuition app
Suppose a Pune tutor wants a prototype for booking trial classes. Consider three synthetic requirements:
- Select a slot: value 2, uncertainty 0; available times are already defined.
- Receive an AI-written study plan: value 1, uncertainty 2; inputs and evaluation criteria are unsettled.
- Choose a profile theme: value 0, uncertainty 0; it does not affect booking.
Build slot selection first. Postpone the study plan until a separate exercise establishes which inputs are necessary and what makes a plan useful. Leave themes outside this cycle.
Evaluate the narrowed build using five fictional booking attempts. Can each attempt select a valid slot, identify unavailable times, and reach an unambiguous confirmation? Record failures rather than adding features to disguise them.
Stop when the core task remains unclear
Stop the build cycle if nobody can describe a successful booking in observable terms. The next step is requirement clarification, not another broad generation request.
If additional access is genuinely needed, check the coding catalog for access format and saved terms before buying. Marketplace account/code/token offers are not proof of official subscription equivalence.
Sources and scope
Source material checked 29 October 2026; features/policies may change. Lovable credits and usage establishes the shared balance and activity-dependent usage context. The scoring worksheet and tuition example are editorial advice, not measured costs or hands-on results.