Lovable advocates for model-specific optimization over generic model selection

Lovable argues against generic model pickers, proposing that effective AI applications require tailoring instructions, tools, and context to the specific strengths of each underlying model. Instead of interchangeable swapping, they use model-specific engineering to ensure consistent performance across different LLMs.