Generative UI Builder

AI-Powered Interface Generation System

Generative UI Builder is a SaaS project that transforms natural language prompts into production-ready UI using structured component definitions and BAML scripting. The goal was to explore how AI can move beyond simple code generation and become a reliable interface-building tool.

Problem

AI code generation tools produce inconsistent, unstructured output that requires significant manual cleanup. They lack understanding of component hierarchies, design systems, and the relationship between UI structure and user experience. The result is code that technically works but is not production-ready.

Solution

Rather than generating raw code, the system uses a structured component registry with defined props, constraints, and composition rules. Natural language prompts are parsed through BAML functions that map intent to component trees. The output is a structured UI specification that can be rendered directly or exported to framework-specific code.

My Role

I designed and built the entire system: the component registry architecture, the BAML prompt pipeline, the parsing and validation layer, and the rendering engine. I also built the interactive playground where users can describe interfaces in natural language and see them rendered in real time.

Technical Architecture

The system uses a three-layer architecture. The component registry defines available UI elements with typed props, constraints, and composition rules. The BAML prompt layer translates natural language into structured component trees using typed LLM functions. The rendering engine takes validated component trees and produces live previews using React and Tailwind CSS.

Key Features

Structured Component Registry

Every UI element is defined as a typed component with explicit props, default values, child constraints, and composition rules. This gives the AI a constrained design space to work within, dramatically improving output consistency compared to unconstrained code generation.

BAML Prompt Pipeline

Natural language prompts are processed through typed BAML functions that extract intent, map to available components, and assemble valid component trees. Each function has defined inputs and outputs, making the prompt pipeline testable and iterable. The BAML layer acts as a typed boundary between unstructured language and structured UI.

Real-Time Preview

The rendering engine takes validated component trees and produces live React previews styled with Tailwind CSS. Users can iterate on prompts and see changes instantly, creating a tight feedback loop between intent and result.

Tiptap Integration

For text-heavy interfaces, I integrated Tiptap as a rich text editor that can be invoked through the same prompt pipeline. This allows the system to handle both structured UI generation and rich text content within a unified architecture.

Tech Stack

Next.js, React, TypeScript, BAML, Tailwind CSS, Tiptap

Outcome

The project demonstrated that structured component registries combined with typed LLM functions can produce significantly more reliable UI generation than raw code generation approaches. It validated the hypothesis that constraining the AI's output space through well-defined component schemas leads to production-quality results. The system remains a strong proof-of-concept for AI-assisted interface building.