Tried Lovable.dev last week. Typed “make me an app showing restaurants from my CSV for wedding friends.” 30 seconds later: full app. Working filters. Data loaded. Ready to deploy.
First thought: magic. Second thought: definitely not magic.
Table of contents
Open Table of contents
One Prompt? Probably Not
I seriously don’t buy that my single sentence went straight to Claude and magically built a complete app from scratch. Behind that simple text box there’s almost certainly a sophisticated orchestration of multiple prompts working together, and I’d bet money on that architectural approach.
The user request gets broken down into structured components immediately upon submission. This isn’t magic at all, just really good prompt engineering executed at scale with careful system design.
The Hidden Prompt Stack (My Best Guess)
Step 1 - Understanding the user intent: The first prompt analyzes my vague request and translates it into structured requirements like “Restaurant list viewer functionality, CSV import capability, shareable link generation, wedding context suggests clean elegant UI design.” This step breaks down my vague human idea into machine-actionable structured requirements.
Step 2 - Planning the component architecture: A planning prompt likely references a UI_STANDARDS.md file and outputs a structured JSON list of all needed UI components without generating any actual code yet. This explains the incredibly consistent Lovable feel across all generated apps because Claude gets carefully crafted standards files that specify exactly how to build each component type consistently.
Step 3 - Generating individual components: Each component identified in the JSON plan gets its own dedicated generation prompt that references appropriate standards files. There’s probably CARD_STANDARDS.md defining how cards should look and behave, LAYOUT_STANDARDS.md for responsive layouts, FILTER_STANDARDS.md for filter UI patterns, and so on. Following the same rules consistently produces polished professional output rather than random inconsistent components.
Step 4 - Assembling everything into a working application: A final code agent assembles all the generated components into a complete functional codebase, initializes a Git repository, and runs the build process. There’s probably even a Claude.md file explaining “here’s exactly how to handle build errors and dependency issues.” This creates consistency at absolutely every layer of the stack.
Why This Matters
The entire sophisticated prompt stack is completely invisible to end users. You type one simple sentence into a text box and see one beautifully polished result appear. But underneath that simple interface there are probably dozens of specialized prompts executing in sequence, with each one being highly specific and structured, each one referencing appropriate standards files for consistency, and each one doing exactly one job really well.
This represents what truly great prompt engineering looks like in production, which isn’t “ask the AI nicely and hope for the best,” but rather “architect a sophisticated system with crystal-clear instructions, explicit constraints, and well-defined success criteria at every step.” The magic isn’t the AI model itself being somehow special, the magic is the invisible prompt architecture that makes the entire complex system feel effortlessly simple.
What We Can Learn
If you’re building anything serious with AI, this architectural approach should be your model for how to structure complex systems.
Don’t do this: Throw raw user input directly at an LLM and pray that it somehow understands what you want and produces good output consistently.
Do this instead: Build a coordinated system of specialized prompts working together, break complex tasks down into manageable sequential steps, use standards files to ensure consistency across all outputs, and make each individual step predictable and verifiable.
Lovable feels magical to users because the team probably spent months painstakingly making all their behind-the-scenes prompts incredibly specific and well-structured. I’d estimate they have 20+ carefully crafted markdown files defining exactly how every single piece of the system should work.
You can build the same level of quality in your own projects by writing comprehensive markdown instruction files, building robust systems rather than fragile one-off prompts, and making all the underlying complexity completely invisible to your end users.
My Theory Could Be Wrong
To be completely transparent, maybe Lovable actually does something entirely different under the hood. Maybe they use one giant comprehensive prompt, or fine-tuned models trained specifically for app generation, or some other approach I haven’t considered, since I have absolutely no insider knowledge.
But based on my prompt engineering knowledge and experience, combined with the remarkable output consistency and fast shipping velocity they’ve demonstrated, I’d confidently bet on them using a sophisticated hidden prompt stack.
The best-engineered prompts don’t feel like you’re interacting with prompts at all. They feel like pure magic working seamlessly. That’s exactly how you know they’re actually working properly.