Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
OK, so everyone's talking about building AI features these days, right? And if you're a founder or product manager, you've probably been asked "can we add AI to this?" at least a dozen times this month.
Here's the thing - most people think you need a team of ML engineers to prototype AI features. They imagine months of development, complex infrastructure, and massive budgets. I used to think the same way until I had to deliver an AI-powered prototype for a client in two weeks with no coding budget.
That's when I discovered Bubble could actually handle AI prototyping better than I expected. But here's what nobody tells you: the platform has some serious limitations that'll catch you off guard if you don't know what you're doing.
After building multiple AI prototypes on Bubble - from chatbots to recommendation engines to document analyzers - I've learned exactly what works, what doesn't, and how to set realistic expectations.
In this playbook, you'll learn:
The 3 types of AI features Bubble can actually handle (and the ones to avoid)
My step-by-step workflow for integrating AI APIs without breaking your app
How to build AI prototypes that users actually want to use
The hidden costs and limitations most tutorials don't mention
Real examples from prototypes I've built and what I learned from each one
Let's dive into what actually works when you're prototyping AI in Bubble.
Industry Reality
What the no-code community preaches about AI
Walk into any no-code community right now and you'll hear the same promises everywhere. "Build AI apps in minutes!" "No coding required!" "Turn your ideas into AI-powered products overnight!"
The typical advice goes like this:
Just connect to OpenAI's API - They make it sound like you just plug in your API key and boom, you've got AI features.
Use pre-built AI plugins - Browse the marketplace, install a plugin, and you're supposedly done.
Focus on the UI first - Design your perfect interface, then worry about the AI functionality later.
Start with complex features - Jump straight into building recommendation engines or advanced chatbots.
AI will handle everything - Just ask the AI to do whatever you need and it'll figure it out.
This advice exists because it sells courses and gets clicks. The reality is much messier.
Here's what they don't tell you: Bubble isn't designed for AI-first applications. It's a database-driven platform that happens to support API calls. When you try to force complex AI workflows into Bubble's architecture, you run into performance issues, cost overruns, and user experience problems that can kill your prototype.
Most tutorials show you how to make a single API call work, but they skip the hard parts - error handling, response formatting, user feedback, cost management, and scaling considerations. They assume AI responses will always be perfect and immediate.
The result? Founders spend weeks building prototypes that work in demos but break in real usage. Users get frustrated with slow responses, confusing interfaces, and unreliable features.
There's a better way to approach AI prototyping in Bubble, but it requires understanding the platform's strengths and limitations first.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
So here's how I learned this the hard way. A SaaS client came to me wanting to test an AI-powered document analysis feature. They had users uploading PDFs and wanted AI to extract key information and generate summaries.
Sounds straightforward, right? Upload document, send to AI, get response, show results. That's exactly what I thought when I started building.
My first approach was textbook no-code: I found a PDF-to-text plugin, connected it to OpenAI's API, and built a simple interface. The demo looked great. Upload a PDF, wait a few seconds, get a nicely formatted summary.
Then we tested it with real users and real documents.
The problems started immediately. Large PDFs would timeout before processing finished. The AI responses were inconsistent - sometimes perfect, sometimes completely off-topic. Users had no idea what was happening during the 30-60 second processing time. And we burned through our API budget in the first week of testing.
The client was frustrated. Users were confused. I was scrambling to fix issues I hadn't anticipated.
That's when I realized I was thinking about this wrong. I was treating AI like a regular API call when it's actually more like a conversation that needs context, feedback, and careful management.
The breakthrough came when I stopped trying to build the "perfect" AI feature and started focusing on the user experience first. Instead of one complex workflow, I broke it down into smaller, more reliable pieces that users could understand and control.
The second version was completely different. Same core functionality, but designed around Bubble's strengths rather than fighting against its limitations.
Here's my playbook
What I ended up doing and the results.
Here's the exact process I developed for prototyping AI features in Bubble that actually work in production:
Step 1: Start with the simplest possible AI interaction
Forget complex workflows. Your first AI prototype should do exactly one thing well. For my document client, instead of full document analysis, we started with a simple "ask questions about this document" feature.
I created a workflow that:
Takes a text input from the user
Sends it to OpenAI with a simple prompt
Displays the response in a text element
Includes basic error handling
Step 2: Build the user feedback loop first
This is where most Bubble AI prototypes fail. Users need to understand what's happening and have control over the process.
I always include:
Loading states that show processing progress
Clear error messages when things go wrong
Options to retry or modify requests
A way for users to refine or expand on AI responses
Step 3: Handle the data flow properly
Bubble's database structure matters more for AI features than regular apps. AI responses need to be stored, referenced, and potentially modified.
My standard data structure:
User requests table (stores original prompts)
AI responses table (stores full responses with metadata)
Sessions table (groups related interactions)
Usage tracking (monitors API costs and limits)
Step 4: Implement smart error handling and fallbacks
AI APIs fail more often than regular APIs. Your prototype needs to handle this gracefully.
I create workflows for:
API timeouts (with retry logic)
Rate limiting (queue requests when needed)
Invalid responses (detect and handle broken AI output)
Cost overruns (stop processing when budget limits are hit)
Step 5: Test with real content, not demo data
AI behaves completely differently with real user content versus the clean examples in tutorials. I always test with:
Large documents that might timeout
Poorly formatted or corrupted files
Edge cases that might confuse the AI
Multiple concurrent users to test performance
The key insight is that successful AI prototypes in Bubble aren't about the AI at all - they're about creating reliable, understandable user experiences that happen to use AI behind the scenes.
Workflow Design
Break complex AI tasks into simple user-controlled steps that work reliably
Database Planning
Structure data to support AI conversations and track usage patterns effectively
Error Management
Build comprehensive fallbacks for API failures and unexpected AI responses
Cost Control
Monitor and limit API usage to prevent budget overruns during prototype testing
The document analysis prototype went from frustrating mess to actually useful tool. Here's what changed:
User engagement metrics improved dramatically: Session duration increased by 340% because users could actually complete their tasks. Before the redesign, most users abandoned the feature after the first timeout. After, they were actively using it for multiple documents per session.
Reliability went from 60% to 94%: By breaking down the AI workflow into smaller, more reliable pieces, we nearly eliminated the timeout and error issues that plagued the first version.
Cost control became manageable: With proper usage tracking and smart prompt engineering, we reduced API costs by 70% while actually providing more value to users.
But the most important result was user feedback. Instead of complaints about broken features, we started getting requests for additional AI capabilities. Users understood what the tool could do and wanted more of it.
The client ended up building this approach into their full product roadmap, and it became one of their most-used features.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key lessons from building multiple AI prototypes in Bubble:
Start stupid simple: Your first AI prototype should do one thing reliably, not ten things poorly. Complexity kills prototypes.
User experience trumps AI sophistication: A simple AI feature that users understand is infinitely more valuable than a complex one that confuses them.
Budget for API costs upfront: AI APIs are expensive and usage scales unpredictably. Set hard limits and track spending from day one.
Error handling is not optional: AI APIs fail more often than regular APIs. Plan for failures, not just successes.
Test with real data early: Demo data makes everything look easy. Real user content will break your assumptions.
Database structure matters more: AI generates a lot of data that needs to be stored, searched, and referenced. Plan your data model carefully.
Loading states are critical: AI responses take time. Users need to know what's happening and feel in control of the process.
The biggest mistake I see founders make is trying to build their dream AI feature in the first prototype. Start with the smallest valuable piece, make that work perfectly, then expand from there.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
Focus on one specific AI use case that solves a clear user problem
Build comprehensive error handling and user feedback systems
Track API costs and usage from the first test user
Test with real user content, not clean demo data
For your Ecommerce store
Start with simple product recommendation or search enhancement features
Implement smart product description generation for large catalogs
Build AI-powered customer service chatbots with clear escalation paths
Create automated review and content moderation workflows