AI Recipe Card Standardizer
Inspiration: Food bloggers are a large group with recipes as their core assets. Converting unstructured recipe text into beautiful, consistent, search-engine-friendly cards (with Schema.org markup) is repetitive labor.
Target Customers: Food bloggers, recipe website operators, nutritionists.
Pain Points:
- Inconsistent Formatting: Recipe drafts written in random formats require manual organization into standard "ingredients" and "steps" formats before publishing.
- SEO Deficiency: Non-technical bloggers don't know how to add
Recipe
Schema.org markup to recipes, missing opportunities to appear as rich snippets in Google search results, losing significant traffic. - Poor Appearance: Want to embed beautiful recipe cards in blog posts but don't understand CSS design.
Solution (Micro-SaaS): An AI tool designed for food bloggers. Users paste recipe text in any format, AI automatically parses, standardizes, and generates beautiful recipe card code containing all necessary SEO markup.
MVP Core Features:
- Text Paste Area: Users can directly paste recipe content copied from notes or documents.
- AI Smart Parsing: AI (LLM) automatically identifies and separates: title, description, prep time, cooking time, servings, ingredient list, step instructions.
- Structured Editor: Display parsed results in clear interface, allowing users to easily edit and correct.
- Template Selection: Provide several different recipe card design style templates.
- One-Click Copy Code: Generate HTML/CSS code (or WordPress Shortcode) that users can directly paste into their blog editors. This code automatically includes all
Recipe
Schema.org JSON-LD markup. - Generate Image: (Optional) Render generated recipe card as image for social media sharing.
Development Investment (Technical Implementation): Low. Pure LLM application with simple backend logic.
- LLM API Calls:
- Core Parsing & Generation: GPT-4 Turbo or Claude 3 Sonnet very suitable for this task. Prompt is key - need to instruct model to act as "food editor," receive unformatted text, then output structured JSON data (containing all fields) and Schema.org-compliant JSON-LD.
- Hugging Face Open Source Models:
meta-llama/Llama-3-8B-Instruct
after minimal sample fine-tuning can fully handle this structured data extraction task.
- Core Technology:
- Backend receives text, calls LLM API, then uses simple template engine (like EJS or Handlebars) to render LLM-returned JSON data into HTML code and JSON-LD scripts.