Automating Visual Form Generation with AI‑Generated DSL

Many teams face the problem that product or operations staff must configure visual‑form systems based on a single sentence requirement, which leads to high configuration cost and communication overhead. This article proposes using AI to understand, decompose, and implement such one‑sentence demands by automatically generating a domain‑specific language (DSL) – in this case a JSON Schema – that drives the visual form engine.

Key ideas

Help AI better understand the input.

Define clear output rules for the AI.

2.1 Chain splitting

The process is split into two main steps: first, let the AI summarize the required form components and field meanings; second, create example component usage (a “few‑shot” prompt) so the AI can learn the expected format.

2.2 Few‑shot prompting

A prompt is crafted based on a reference project (Mr‑Ranedeer‑AI‑Tutor) that uses a structured prompt to guide the AI in generating the desired JSON. The prompt includes a description of the task, the required JSON structure (a components array and a requirement field), and example component configurations.

2.3 Fake answer (imitated dialogue)

Three roles – System, AI, Human – are used to define a fake human answer that serves as a template for the AI’s response. This ensures the AI outputs the JSON in the exact format required.

2.4 Optimization strategy

To reduce token consumption, only the necessary component list is provided to the AI. Errors such as using items instead of enum for select options are corrected by feeding the AI targeted learning cases and additional prompt rules.

3 Real‑time push

For smooth interaction, the article adopts a Server‑Sent Events (SSE) approach on the server side and fetch-event-source on the client side. The required server headers are:

res.setHeader('Content-Type', 'text/event-stream; charset=utf-8');
res.setHeader('Cache-Control', 'no-transform, no-cache');
res.setHeader('Connection', 'keep-alive');

When writing data, each chunk must follow the data: ${message}\n\n format.

3.2 Local proxy issues

If the stream is interrupted, check that the Cache-Control: no-transform header is present; a local proxy may otherwise block continuous transmission.

3.3 Nginx reverse proxy

When using Nginx, disable buffering and extend timeout values to accommodate long AI generation times:

location /api {
   # Disable buffering for real‑time streaming
   proxy_buffering off;
   # Increase timeouts for AI‑generated content
   proxy_connect_timeout 600;
   proxy_send_timeout 600;
   proxy_read_timeout 600;

   proxy_pass http://127.0.0.1:9000;
}

Summary

The article demonstrates that by iteratively refining prompts and providing concrete component rules, AI can reliably generate the JSON/YAML DSL needed for low‑code visual form systems. Continuous prompt optimization and proper server configuration (SSE, proxy settings) are essential for achieving smooth, real‑time interactions.