How to Add AI Content Generation to a Bubble.io App
A practical guide to building AI content generation features in Bubble.io — covering text generation, prompt design, content types, quality control, and the UI patterns that make AI writing features feel professional.
How Content Generation Works in Bubble
All AI content generation follows the same basic pattern — what differs is the prompt design and output handling.
Collect structured input
Gather the information the AI needs: product name, tone, target audience, key features, word count. The more structured and specific your input, the better the output.
Build a dynamic prompt
Combine your input fields with prompt instructions to create a complete, specific request. Store the prompt template in your database so you can update it without republishing.
Call the AI API
Pass the assembled prompt to OpenAI or Claude via the API Connector. Set appropriate max_tokens for the content type.
Store and display the output
Save the generated content to your database. Display it in a rich text editor or formatted text element, allowing users to review and edit before publishing.
AI Generation Patterns for 6 Content Types
Each content type requires a different prompt structure and output handling approach.
Blog Post / Article
Pass: topic, keywords, target audience, word count, tone. Prompt: ‘Write a [word_count]-word blog post about [topic] targeting [audience]. Include these keywords naturally: [keywords]. Tone: [tone]. Use H2 subheadings.’ Store in rich text field.
Product Descriptions
Pass: product name, features (list), target buyer, brand voice. Prompt: ‘Write a compelling [length]-word product description for [name]. Key features: [features]. Write for [buyer]. Brand voice: [voice].’ Great for e-commerce apps.
Email Copy
Pass: email type (welcome/nurture/re-engagement), sender name, user first name, CTA, tone. Prompt: ‘Write a [type] email from [sender] to [name]. Goal: [cta]. Tone: [tone]. Max 200 words.’ Use json_object mode to get subject and body as separate fields.
Social Media Posts
Pass: platform (LinkedIn/Twitter/Instagram), topic, key message, include hashtags (boolean). Prompt: ‘Write a [platform] post about [topic]. Key message: [message]. [hashtag_instruction]. Match platform’s native tone.’ Generate 3 variants for A/B testing.
SEO Meta Content
Pass: page title, main keyword, page content summary. Prompt: ‘Write an SEO meta title (max 60 chars) and description (max 160 chars) for a page about [summary]. Include keyword: [keyword]. Return as JSON: {title: string, description: string}.’
Job Descriptions
Pass: role title, department, responsibilities (list), requirements (list), company tone. Prompt: ‘Write a job description for [title] at a [tone] company. Responsibilities: [list]. Requirements: [list]. Make it compelling and inclusive.’ Store sections separately.
The Patterns That Produce Consistent Output
Good prompt engineering is the difference between AI content that impresses and AI content that frustrates.
What Works
- Specify format explicitly (“Return as 3 bullet points”, “Use H2 headings”)
- Specify length explicitly (“approximately 150 words”, “max 3 paragraphs”)
- Give examples of good output in the prompt for consistent style
- Tell the AI what NOT to do (“Do not include clichés like ‘game-changing'”)
- Request JSON output when you need structured data fields
What Hurts Quality
- Vague tone instructions (“write professionally”) — give examples instead
- Combining too many tasks in one prompt — split complex tasks into steps
- No word count guidance — AI fills available tokens, output varies wildly
- Missing context — the AI cannot write well about something it knows nothing about
- Hardcoded prompts — store templates in DB so you can iterate quickly
Ensuring AI Content Meets Your Standards
AI content generation requires guardrails, especially in user-facing or published contexts.
Review Before Publish
Never auto-publish AI content. Always route generated content through a review state where a human confirms before it goes live. Add a ‘Review’ button that sets status from generated to published.
Regenerate Option
Always give users a ‘Regenerate’ button that calls the API again with the same inputs. Add a ‘variation’ parameter that increments so the AI produces a different take rather than the same output.
Output Validation
Check minimum length, detect if the AI returned an error message instead of content, and verify JSON structure if you requested structured output. Flag failed generations and retry automatically once.
Need AI Content Generation Built Into Your App?
SA Solutions builds production-grade content generation features — with proper prompt engineering, quality controls, and UX that makes AI writing feel like a native part of your product.
