How to Map Out Your Database Structure Using Claude AI (Before Writing Any Code)

Quick Summary

Before jumping into coding, I spent day 6 of my 60-day challenge mapping out the complete database structure for my Bali travel directory using Claude AI. Here's how I got a crystal-clear overview of all tables, relationships, and data fields without writing a single line of SQL.

Introduction

We're almost one week into building this travel platform, and I hit a crucial realization. Instead of diving straight into page templates like I originally planned, I needed to step back and properly map out the database structure first.

Why? Because understanding what data you need to store completely changes how you design your pages. It's like trying to build a house without knowing what rooms you need – you'll end up redesigning everything later.

Today I'm going to show you exactly how I used Claude AI to create a comprehensive database blueprint that will guide the entire development process.

The Challenge

Building a travel directory means handling tons of different data types. We need to store information about:

• Hotels and accommodations

• Restaurants and beach clubs

• Activities and experiences

• User accounts and reviews

• Locations and amenities

• Booking links and affiliate partnerships

The challenge isn't just storing this data – it's understanding how everything connects together. A hotel might have multiple restaurants, activities happen in specific locations, and users need to leave reviews for different types of listings.

Without mapping this out properly, you'll end up with a messy database that's impossible to maintain.

Our Approach

Instead of guessing what data fields we need, I decided to:

1. Use Claude AI as a project manager to analyze our requirements

2. Research existing travel platforms to see what amenities they track

3. Create a complete database overview before writing any SQL

4. Map out all relationships between different data types

5. Generate detailed implementation guidelines for developers

The key insight here is using AI not just to write code, but to think through the entire system architecture from a project management perspective.

Step 1: Setting Up Claude for Strategic Thinking

First, I opened my Claude project where I'd already uploaded our technical specifications and URL structure from previous days. This context is crucial – Claude needs to understand the full scope of what we're building.

I specifically asked Claude to act as a project manager rather than a developer:

"I want to work on the whole database structure. I don't want you to create database tables yet – I just want a complete overview of everything we need. All possible tables without actually writing SQL."

The important thing here is being explicit about what you want. I didn't want code yet – I wanted strategic thinking about data architecture.

Step 2: Analyzing Real Travel Platforms

While Claude was processing, I opened Booking.com to research what data fields successful travel platforms actually use. This is something a lot of developers skip, but it's incredibly valuable.

I found features I hadn't considered:

• Room availability with detailed popups

• Discount tracking systems

• FAQ sections per listing

• On-site restaurant information

• Traveler questions and answers

This real-world research ensures we don't miss important features that users expect from travel websites.

Step 3: Getting the Complete Database Overview

Claude delivered exactly what I needed – a comprehensive breakdown of every table we'd need:

Core Tables:

• Listings (main content like hotels, restaurants)

• Locations (geographic data with coordinates)

• Categories (stays, dining, activities, wellness)

• Users (account management and roles)

• Reviews (with media upload capability)

Relationship Tables:

• Listing amenities (many-to-many relationships)

• Location hierarchies (regions, areas, specific spots)

• User permissions and roles

• Media files linked to listings and reviews

Smart Details I Wouldn't Have Thought Of:

• Affiliate booking links for each listing

• Menu upload capability for restaurants

• Geographic coordinates for "nearby" searches

• Publish/draft/archive status workflow

Step 4: Refining Based on Real Platform Research

After getting Claude's initial output, I fed back insights from Booking.com:

"We should add discount items to tables, FAQ sections per listing, and menu upload capabilities for restaurants. Also, we need affiliate link storage since we're not handling bookings directly."

Claude immediately updated the database structure to include these real-world requirements. This back-and-forth process is where AI really shines – it can quickly iterate on complex structures.

Step 5: Creating Implementation Guidelines

The final step was asking Claude to create detailed developer documentation:

"Act as a project manager and scope this out as detailed as possible so developers understand what they need to do, how everything relates to each other, and clear implementation instructions."

This generated a complete implementation guide that I can upload directly into Cursor AI, including:

• Database relationships diagram

• Table structure with field types

• Implementation priorities

• Technical considerations for Supabase

Challenges We Faced

Challenge 1: Database ID Format
Claude initially used standard "ID" fields, but Supabase uses UUID format. I had to correct this to avoid issues later.

Challenge 2: Feature Creep
It's tempting to add every possible feature. I had to balance completeness with keeping the initial version manageable.

Challenge 3: Missing Context
Some suggestions didn't make sense until I provided more context about our specific use case (affiliate links vs. direct bookings).

How We Overcame Them

For technical details: I specifically mentioned we're using Supabase, so Claude adjusted the database structure accordingly.

For scope management: I focused on core features first, noting that we can always add more amenities and fields later through an admin dashboard.

For context issues: I kept referring back to our uploaded specifications and keyword research to keep suggestions relevant.

Results So Far

We now have a complete database blueprint that includes:

• 15+ interconnected tables with clear relationships

• 200+ predefined amenities based on our keyword research

• Complete implementation guide ready for Cursor AI

• Real-world features from successful travel platforms

Most importantly, we have a clear foundation before writing any code. This will save hours of refactoring later.

Key Takeaways

• Map your database structure before designing pages – your data determines your design possibilities

• Use AI as a strategic thinking partner, not just a code generator

• Research successful platforms in your niche to avoid missing obvious features

• Be specific about what you want from AI – project management vs. development are different skills

• Iterate based on real-world research rather than theoretical requirements

What's Next

Tomorrow we're mapping out the actual page layouts – where the H1 goes, how we display amenities, where call-to-action buttons should be placed. With our database structure locked in, we can design pages that perfectly match our data capabilities.

Then it's one more day of design inspiration before we start building the actual platform with Cursor AI.

Conclusion

Taking time to properly plan your database architecture isn't glamorous, but it's absolutely critical. Spending one day on strategic planning saves weeks of refactoring later.

The combination of AI strategic thinking and real-world platform research gave us a database structure that's both comprehensive and practical. Now we can move forward with confidence, knowing our foundation is solid.

Want to Learn More?

I'm documenting this entire 60-day journey of building a travel platform from scratch. If you want to see how this database structure translates into actual pages and functionality, make sure to follow along with the series. Tomorrow we're diving into page layout mapping – the bridge between data structure and user experience.