Lovable: Plan Mode
LovableLovable introduced Plan Mode — a major rethinking of how users interact with AI before code is ever written. Instead of immediately generating an app from a prompt, users can now review and edit Lovable's proposed implementation strategy in a dedicated view, approve it, and only then let the agent build. Approved plans are saved to .lovable/plan.md for persistent context across sessions. The feature also arrived alongside a prompt queue system that lets users stack, reorder, and repeat prompts.
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Plan Mode: Review Before You Build
Lovable shipped Plan Mode on February 5, 2026, fundamentally changing the moment between describing an idea and watching code get written. Rather than immediately generating an application from an initial prompt, Lovable now pauses to present a detailed implementation plan — one the user can read, edit, and refine before giving the green light.
How Plan Mode Works
When a user submits a prompt, Lovable drafts a structured plan describing what it intends to build: which components to create, what logic to implement, how data flows through the application. This plan appears in a dedicated review view, where the user can make edits, add constraints, or remove directions before approving it. Once approved, the plan is saved to .lovable/plan.md, giving the AI persistent context it can reference across future sessions.
The practical benefit is significant. Users previously had little visibility into how Lovable interpreted an ambiguous prompt until code started appearing — and correcting a misunderstood direction could cost multiple build credits. Plan Mode moves that correction opportunity to before any tokens are spent on generation.
Prompt Queue with Repeatable Items
Alongside Plan Mode, Lovable shipped a redesigned prompt queue system. Rather than sending one prompt and waiting idly, users can now build a queue of prompts that Lovable processes sequentially. Each item in the queue can be paused, reordered, edited, copied, removed, or repeated — up to 50 times for automating repetitive tasks.
This is particularly useful for developers iterating rapidly on a feature: queue a series of refinements, step away, and return to a fully executed sequence of changes.