How to Avoid Pitfalls When Using GPT-Image2 Prompts: Template-Driven Best Practices

Avoid GPT-Image2 prompt pitfalls by using atomic JSON templates from docs/templates.md, filling every required field explicitly, and implementing the credit reservation flow in api/generate-image.js to catch errors before generation begins.

The GPT-Image2 system treats prompts as executable code rather than suggestions, requiring strict structural adherence to prevent model confusion. According to the freestylefly/awesome-gpt-image-2 repository, this Prompt-as-Code philosophy divides specifications into atomic sections (subject, style, layout) but introduces specific failure modes when fields are under-specified or contradictory. Understanding the architecture behind the 21 industrial-grade templates and the API's reservation system is essential for reliable image generation.

Understanding the Prompt-as-Code Architecture

The repository implements a rigorous structure designed for programmatic reliability. Unlike conversational GPT models, GPT-Image2 requires explicit constraints to avoid visual hallucinations and platform mismatches.

Template System Foundations

The core intelligence resides in docs/templates.md, which defines 21 industrial-grade templates across categories including UI, infographic, poster, product, brand, architecture, photography, illustration, character, scene, history, and document. Each template contains a "避坑指南" (pitfall-avoidance guide) that mandates minimal required fields and identifies typical sources of model confusion. For example, the UI template explicitly requires platform specification (iOS/Android/Web) and layout definition to prevent aspect ratio defaults that distort screenshots.

API and Reservation Flow

Before invoking the CIYUAN image service, api/generate-image.js executes a credit reservation via the Supabase RPC reserve_generation_usage. This guarantees users cannot exhaust quotas with malformed prompts. The handler surfaces specific error codes—CREDITS_REQUIRED, UPSTREAM_BUSY, or GENERATION_FAILED—allowing clients to react appropriately before wasting computational resources. The reservation commits only after successful generation; otherwise, the system releases credits with descriptive error metadata.

Common GPT-Image2 Prompt Pitfalls

Analysis of the template guides reveals five critical failure patterns that degrade output quality.

Vague Platform Specifications

Prompts requesting "screenshots" without specifying iOS, Android, or Web trigger inconsistent aspect ratios and UI element hallucinations. The UI template's pitfall guide forces explicit platform and layout fields, preventing the model from defaulting to generic 9:16 mobile compositions that mismatch desktop interfaces.

Text Readability Failures

The model frequently generates placeholder glyphs or incorrectly translates Chinese text when not explicitly constrained. The "强制文字锁定" (force text lock) clause—mandatory in the UI template—requires the exact Chinese phrase and explicitly forbids gibberish, ensuring text remains absolutely readable in the final output.

Cross-Platform Visual Contamination

Mixing visual cues from different platforms—such as placing X-Twitter verification badges on WeChat screenshots—creates cognitive dissonance in the model. The UI template demands platform-specific visual cues: blue checks for Twitter, music discs for Douyin, and distinct navigation patterns for each ecosystem.

Layout Overload

Infographic templates receiving long paragraphs cause the model to attempt fitting all content, producing cluttered, unreadable diagrams. The infographic pitfall guide enforces strict "模块数量" (module count) and "图表类型" (chart type) limits, forcing content truncation and hierarchical information design.

Uncontrolled Styling

Omitting explicit style or color palette parameters yields unpredictable aesthetics across generations. Every template requires a style block defining primary/secondary colors, themes, and mood to maintain visual consistency.

Practical Implementation Guide

Implement these four steps to eliminate generation failures when integrating GPT-Image2 into production workflows.

Step 1: Select the Correct Template

Start from the category list in docs/templates.md and copy either the plain-text or JSON skeleton matching your target output. Do not construct prompts from scratch; the templates encode necessary structural constraints that prevent model confusion.

Step 2: Atomic Field Completion

Replace every [placeholder] with concrete values. Never leave optional fields blank—specifically define platform, colors, subject_description, and aspect_ratio. The JSON templates programmatically enforce this by requiring specific keys in the object structure.

Step 3: Text Locking and Validation

When generating Chinese text, append the constraint clause: "文字必须绝对可读，必须显示指定的中文" (text must be absolutely readable and must display the specified Chinese). This prevents the model from generating untranslated placeholder characters.

Step 4: API Integration with Error Handling

Always verify quota availability using the GET endpoint before POSTing full generation requests. The api/generate-image.js handler returns quota status and free-generation flags, allowing you to validate required fields client-side before consuming credits.

Code Examples

The following implementations demonstrate correct workflow integration using Node.js fetch patterns.

Plain-Text UI Prompt Implementation

This example queries the quota endpoint, handles the CREDITS_REQUIRED error, and submits a fully-specified UI prompt to api/generate-image.js:

// Install node-fetch if needed: npm i node-fetch
import fetch from 'node-fetch';

const prompt = `为电商App生成一张iOS界面图。
核心功能：商品浏览、购物车、支付。
视觉风格：极简，主色#1A73E8，强调色#FFC107。
布局：顶部导航/卡片流，信息层级清晰，留白充足。
输出：高保真UI截图，文字清晰可读，比例9:16。`;

async function generate() {
  // First, check quota (GET)
  const status = await fetch('https://your-domain/api/generate-image', { method: 'GET' });
  const info = await status.json();
  if (!info.ok && info.error === 'CREDITS_REQUIRED') {
    console.error('Not enough credits'); return;
  }

  // POST the prompt
  const res = await fetch('https://your-domain/api/generate-image', {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify({ prompt, caseId: 1 })   // caseId is any catalog entry you’re using
  });

  const data = await res.json();
  if (!data.ok) {
    console.error('Generation error:', data.error);
    return;
  }

  // `data.image` is a data-URL; write it to a file if you like
  const base64 = data.image.split(',', 2)[1];
  require('fs').writeFileSync('output.jpg', Buffer.from(base64, 'base64'));
  console.log('Image saved as output.jpg');
}

generate();

Structured JSON Template Approach

For agent automation, use the JSON template variant to guarantee field completeness. The API treats the entire JSON string as the prompt payload:

import fetch from 'node-fetch';

// JSON version of the UI template (see docs/templates.md#tpl-ui)
const jsonPrompt = {
  type: "UI Screenshot",
  platform: "iOS",
  product: "Fitness App",
  layout: "Card-based feed with bottom tab bar",
  style: {
    theme: "Dark Mode",
    primary_color: "Neon Green",
    typography: "Clean sans-serif"
  },
  content: {
    header: "Today's Activity",
    cards: [
      { title: "Running", data: "5.2 km", button: "Start" },
      { title: "Calories", data: "340 kcal" }
    ]
  },
  constraints: "High fidelity, readable text, 9:16 aspect ratio"
};

async function generateJSON() {
  const res = await fetch('https://your-domain/api/generate-image', {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify({ prompt: JSON.stringify(jsonPrompt), caseId: 1 })
  });

  const out = await res.json();
  if (!out.ok) {
    console.error('Failed:', out.error);
    return;
  }

  const b64 = out.image.split(',', 2)[1];
  require('fs').writeFileSync('ui-json.jpg', Buffer.from(b64, 'base64'));
  console.log('Saved UI image from JSON template');
}

generateJSON();

Agents benefit from JSON templates because programmatic field population guarantees no required tokens are omitted, preserving the structure the CIYUAN service expects.

Summary

• Use atomic templates from docs/templates.md to enforce structural constraints that prevent model confusion.
• Lock text explicitly with "强制文字锁定" clauses to ensure Chinese characters render correctly without placeholder glyphs.
• Reserve credits first via the reserve_generation_usage RPC in api/generate-image.js to prevent quota exhaustion from malformed requests.
• Specify platform and layout explicitly to avoid aspect ratio defaults and cross-platform visual contamination.
• Validate with GET requests before POSTing to check for CREDITS_REQUIRED errors and verify server configuration.

Frequently Asked Questions

What causes "unreadable text" errors in GPT-Image2?

Unreadable text occurs when prompts fail to explicitly constrain the model to preserve exact Chinese phrases. According to the UI template pitfall guide in docs/templates.md, you must include the "强制文字锁定" directive to forbid placeholder glyphs and enforce absolute text readability.

How does the credit reservation system prevent prompt failures?

Before generating images, api/generate-image.js calls the Supabase RPC reserve_generation_usage to lock the required credit amount. If the prompt is malformed or the upstream CIYUAN service is busy, the system returns CREDITS_REQUIRED or UPSTREAM_BUSY errors without consuming the reservation, allowing you to fix the prompt before retrying.

Why should I use JSON templates instead of plain text prompts?

JSON templates enforce programmatic completeness. By requiring specific keys like platform, style, and layout, the structure prevents omission of required fields that cause vague outputs. As implemented in freestylefly/awesome-gpt-image-2, agents can reliably populate JSON fields to ensure consistent, repeatable image generation.

What is the "Prompt-as-Code" philosophy?

The Prompt-as-Code philosophy treats image generation prompts as executable specifications rather than suggestions. This approach, central to GPT-Image2, divides prompts into atomic sections (subject, style, layout) using templates from docs/templates.md, ensuring the model receives unambiguous instructions that eliminate visual hallucinations and platform mismatches.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}