Control AI generation with Meku’s LLM Profile Switcher. Choose Basic, Pro, or Max profiles to balance speed, reasoning depth, and credit usage when building apps.
The LLM Profile Switcher allows you to control how Meku’s AI generates your application.
Each profile uses a different model configuration and generation preset. These profiles vary in reasoning depth, structural consistency, generation quality, and credit consumption.
By selecting a profile, you can adjust how the AI approaches your project depending on whether you are prototyping quickly or building more complex applications.

Meku provides three generation profiles designed for different development scenarios.
Credit Usage: 1.0x
Basic is optimized for speed and efficient credit usage.
Best suited for:
This profile focuses on rapid generation with lower computational cost.
Credit Usage: 1.5x
Pro provides stronger reasoning and more consistent UI generation.
Best suited for:
Pro offers a balance between output quality and credit efficiency.
Credit Usage: 2.5x
Max uses the most advanced configuration available in Meku.
Best suited for:
This profile prioritizes deeper reasoning and structural accuracy.
Profiles can be selected directly from the build interface before running a generation or during the iteration.
When a profile is selected:
Switching profiles does not modify previously generated code. It only affects future prompts and iterations.

Choosing the right profile depends on the stage of your project.
Use Basic when:
Use Pro when:
Use Max when:
Profile selection should align with the project's complexity and the level of iteration required.
Each profile applies a multiplier to the base credit usage.
Example:
If a generation requires 100 base credits:
This system ensures transparent resource usage across different AI configurations.
Changing profiles does not affect existing project code.
Switching profiles:
It only influences how future prompts are interpreted and executed.
Profiles can be changed at any time during the project lifecycle.
For efficient development and credit usage, a staged workflow works best.
This approach balances generation quality with credit consumption.