From 0 to 1: Build Personalized AI-Powered Apps with Feishu Multi-Dimensional Tables [Hands-On Guide]
Want to dive into AI app development but don’t know where to start? Feishu Multi-Dimensional Tables offers the perfect launchpad. In this guide, I’ll walk you through leveraging this robust tool alongside cutting-edge AI technologies (like image recognition) to create custom mini-applications tailored to your needs. Whether you’re cataloging life experiences, managing collections, or bringing creative ideas to life, you’ll witness how an innovative product evolves from concept to reality in just a few steps. Let’s embark on this journey of digital craftsmanship!
Case Study: Building a “Private Liquor Museum”
By the end of this tutorial, you’ll create an application that automatically identifies liquor details (name, origin, ABV, tasting notes) simply by uploading product photos.
Step 1: Needs Analysis
As a liquor enthusiast, I often struggle to recall specific details about wines and spirits I’ve tasted. Traditional note-taking—snapping photos, manually logging names, origins, ABV, and flavor profiles—is time-consuming and error-prone. With Feishu Multi-Dimensional Tables + AI, however, I can automate this process: upload a photo, and let AI extract all critical information instantly.
Step 2: Pre-Implementation Checklist
Critical Preparation: • Doubao Vision Pro-32K Model Access: Follow the tutorial shared by community expert “王大仙” (linked in the original article) to secure a Doubao account. New users receive 500,000 free tokens—more than enough for personal projects.
Step 3: Structuring Your Data
3.1 Define Key Fields
Your liquor database should include:
• Name
• Distillery/Brand
• Origin
• ABV (Note: Additional fields like “Original Gravity” can be added for beer-specific entries)
• Tasting Notes
• Recommended Audience
3.2 Create a Multi-Dimensional Table
Initialize a table in Feishu and remove default columns to start fresh.
Step 4: Core Field Configuration
4.1 Photo Upload Field
Set the field type to 【Attachment】 to enable image uploads.
4.2 AI-Powered Liquor Recognition (Key Step)
• Create an 【AI Liquor Analysis】 field using 「Field Shortcuts」→「AI Image Comprehension」. • Link your Doubao Vision Pro-32K model account for advanced image analysis.
Custom Prompt Engineering: To ensure precise outputs, craft a structured prompt for the AI sommelier:
Role: Professional Liquor Sommelier
Objective: Extract and format data from liquor images into:
- Name
- Origin
- Brand
- Type (e.g., whiskey, red wine)
- ABV
- Tasting Notes (aroma, palate, finish)
- Recommended Audience
Constraints: Output "Unknown" for unclear attributes; maintain strict formatting. Example Output:
- Name: Chimay Blue Cap
- Origin: Belgium
- Brand: Chimay
- Type: Trappist Beer
- ABV: 9% vol
- Tasting Notes: Rich malt backbone with hints of dried fruit, clove, and peppery spice. Velvety texture with a bittersweet finish.
- Recommended Audience: Craft beer enthusiasts, adventurous drinkers. Step 5: Testing & Troubleshooting
5.1 Initial Testing
Upload a test image (e.g., a Belgian beer bottle). Common Pitfall: HEIC format images from iOS devices may cause errors—convert to JPEG/PNG first.
5.2 Field Optimization
Use 「Field Shortcuts」→「Content Extraction」 and 「Smart Tagging」 to parse AI outputs into discrete columns (e.g., separate “ABV” from raw text).
Step 6: Advanced Features
6.1 Gallery View
Create a 「Gallery View」 for visual browsing—perfect for showcasing liquor labels and key details at a glance.
6.2 Automated Alerts
Set up 「Automation」→「Notifications」 to receive Feishu alerts when new entries are added or updated.
Step 7: Real-World Application
User Flow Demonstration:
- Upload a photo of Japanese whisky.
- Receive instant notification with AI-generated analysis.
- Review structured data in the table or gallery view.
Accuracy Note: While the AI excels with clear label images, it may return “Unknown” for obscured details (e.g., hidden ABV on bottle backs). Always verify critical data.
Future Enhancements
• Personalized Reviews: Add a “My Tasting Notes” column to blend AI insights with subjective experiences.
• Scalability: Adapt this framework for cosmetics cataloging, fitness equipment tutorials, or any collection-management need.
Why Stop at Liquor?
The same methodology applies to:
• Skincare/Cosmetic Databases
• Fitness Equipment Guides
• Custom Recipe Managers
Start Building Today—Innovation Waits for No One!
Pro Tip: Explore Feishu’s template library for pre-built solutions, or join their developer community to share and refine your AI-powered workflows.