AI & Automation
Personas
Ecommerce
Time to ROI
Short-term (< 3 months)
When I landed a Shopify project with over 3,000 products, I thought the hardest part would be the SEO overhaul. I was wrong. The client's biggest pain point wasn't keyword research or content creation—it was the mind-numbing task of writing alt text for thousands of product images.
Most ecommerce sites either skip alt text entirely or use generic descriptions like "product image" or "blue shirt." This isn't just bad for accessibility—it's leaving SEO opportunities on the table. Search engines can't "see" images without descriptive alt text, and that visual search traffic? Gone.
What started as a routine SEO project became an experiment in AI automation that saved my client hundreds of hours while actually improving their image SEO performance. Here's the exact system I built and why it worked better than any manual process.
In this playbook, you'll learn:
Why traditional alt text workflows fail at scale
The AI automation system I built for 3,000+ products
Specific prompts and workflows that generate SEO-friendly alt text
How this improved both accessibility and organic traffic
Pitfalls to avoid when automating image optimization
This isn't about replacing human creativity—it's about scaling what actually matters. Let me show you exactly how I did it.
Industry Reality
What every ecommerce owner has been told about alt text
The conventional wisdom around image alt text is straightforward and well-intentioned. Every SEO guide tells you the same thing: "Write descriptive, keyword-rich alt text for every image." Accessibility experts emphasize that alt text should paint a clear picture for screen readers. Both are absolutely right.
Here's what the industry typically recommends:
Be descriptive: Include product name, color, style, and key features
Include keywords: Naturally incorporate SEO keywords without stuffing
Keep it concise: Aim for 125 characters or less for optimal display
Consider context: Different alt text for product listings vs. detail pages
Avoid redundancy: Don't duplicate information already in surrounding text
This advice isn't wrong—it's the foundation of good image optimization. The problem is implementation at scale. For a store with 50 products, manual alt text is manageable. For 500 products? It becomes a significant time investment. For 3,000+ products? It's practically impossible to do well manually.
Most ecommerce teams end up in one of these traps: hiring expensive content writers who don't understand the products, using generic templates that hurt SEO performance, or simply ignoring alt text altogether. None of these solutions actually solve the core problem: creating quality, contextual descriptions at scale.
The industry knows what good alt text looks like. What it doesn't have is a scalable way to create it consistently. That's where AI automation changes everything—not by replacing human judgment, but by applying it systematically across thousands of images.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
The client came to me with a Shopify store that had grown organically over several years. They sold home decor and furniture—everything from vintage leather armchairs to minimalist coffee tables. Beautiful products, solid business, but their SEO was a disaster.
Here's what I walked into: 3,000+ products across 8 different languages, with about 80% of images having either no alt text or generic placeholders like "product photo" or "image1.jpg." The few manually written alt texts were inconsistent—some were keyword-stuffed nonsense, others were too vague to be useful.
My first instinct was the traditional approach. I quoted them for manual alt text creation: roughly 40 hours of work at $75/hour just for the English versions. Then multiply that by 8 languages, and we're looking at a $24,000 project just for image optimization. The client nearly fainted.
I tried a middle-ground approach first. I created a template-based system where I'd write 10-15 alt text variations for similar products, then the client's team could adapt them. It was faster than starting from scratch, but still required significant manual work for each product.
The real problem became clear after two weeks of this process: even with templates, maintaining consistency across thousands of products was impossible. The client's team didn't have the SEO knowledge to adapt the templates properly, and they certainly didn't have time to do it for multiple languages.
That's when I realized the fundamental issue wasn't the alt text writing—it was the scalability. No manual process, no matter how well-designed, could handle this volume while maintaining quality. I needed to find a way to apply consistent SEO principles across thousands of images without human bottlenecks.
The breakthrough came when I stopped thinking about AI as a replacement for human writers and started thinking about it as a way to scale human expertise. Instead of writing individual alt texts, I could encode my SEO knowledge into prompts and let AI apply that knowledge consistently across the entire catalog.
Here's my playbook
What I ended up doing and the results.
Here's the exact system I built for automated alt text generation. This isn't theoretical—this is the workflow that processed over 3,000 product images in multiple languages.
Step 1: Product Data Export and Analysis
I started by exporting all product data from Shopify into a CSV file: product title, description, category, price, and existing image URLs. This gave me the raw material the AI would use to understand each product's context. The key insight here was that good alt text isn't just about describing what you see—it's about understanding what the product actually is and how it fits into the customer's search intent.
Step 2: Building the Knowledge Base
Working with the client, I compiled a comprehensive knowledge base about their products. This included industry terminology, material descriptions, style categories, and common customer search terms. For example, a "mid-century modern walnut coffee table" needed specific vocabulary around wood types, design eras, and furniture categories that only someone with product knowledge would know.
Step 3: Custom AI Prompt Architecture
This is where the magic happened. I created a three-layer prompt system:
Layer 1 - SEO Requirements: Keywords to include, character limits, and search intent optimization
Layer 2 - Product Context: Category-specific terminology and feature highlighting
Layer 3 - Brand Voice: Tone and style consistency across all descriptions
The prompt template looked like this:
"Based on this product data: [PRODUCT_INFO], create SEO-optimized alt text that includes the primary material, style category, and main function. Keep it under 125 characters, include relevant keywords naturally, and maintain our premium but approachable brand voice. Focus on features that customers actually search for."
Step 4: Automated Workflow Integration
I built this into a custom AI workflow that could process the entire product catalog. The system would read the product data, apply the appropriate prompt template based on product category, generate the alt text, and output it in a format ready for bulk upload back to Shopify.
Step 5: Quality Control and Iteration
The first batch wasn't perfect. I reviewed a sample of 100 generated alt texts, identified patterns in the AI's mistakes, and refined the prompts. The most common issues were overly technical language and keyword repetition. After three iterations, the output quality was consistently better than the client's existing manual alt texts.
Step 6: Multi-Language Scaling
Once the English version was dialed in, I adapted the prompts for the other 7 languages. This wasn't just translation—it was localization. Different markets search for products differently, so the keyword focus had to shift for each language while maintaining the core SEO principles.
The entire system processed 3,000+ products across 8 languages in about 6 hours of total AI processing time. Compare that to the estimated 320 hours of manual work, and you can see why this approach was transformative.
Prompt Engineering
Custom AI prompts that balance SEO requirements with natural language, tested across 3,000+ products
Workflow Integration
Seamless Shopify API integration that processes product data and uploads optimized alt text automatically
Quality Control
Three-iteration refinement process that improved output quality beyond manual writing standards
Multi-Language
Localized prompt variations for 8 different markets, adapting to regional search behaviors
The results were immediate and measurable. Within 30 days of implementing the AI-generated alt text, the client saw a 23% increase in image search traffic from Google. More importantly, the alt text quality was consistently higher than their previous manual attempts.
Before the automation, only 20% of their product images had meaningful alt text. After implementation, 100% of images had SEO-optimized, contextually relevant descriptions. The time savings were even more dramatic—what would have taken 320 hours of manual work was completed in 6 hours of AI processing.
The accessibility improvements were equally significant. Screen reader users could now understand exactly what each product image showed, improving the overall user experience for visually impaired customers. This wasn't just about SEO—it was about making the site genuinely more inclusive.
Perhaps most importantly, the system was maintainable. When they added new products, the AI workflow automatically generated appropriate alt text without any manual intervention. This solved the long-term scalability challenge that manual processes can't address.
The client's feedback was overwhelmingly positive. They went from dreading image optimization to having it completely automated, freeing up their team to focus on higher-value activities like product sourcing and customer service.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key lessons I learned from automating alt text generation for thousands of product images:
AI quality depends on input quality: Garbage product data produces garbage alt text. Clean, detailed product information is essential.
Industry knowledge can't be automated away: The AI needed human expertise encoded in the prompts to understand product nuances and customer search behavior.
Consistency beats perfection: Mediocre alt text applied to 100% of images outperforms perfect alt text on 20% of images.
Context matters more than description: Good alt text isn't just about what's in the image—it's about what the customer is looking for.
Iteration is crucial: The first AI output won't be perfect. Plan for 3-5 rounds of prompt refinement.
Multi-language requires localization: Don't just translate—adapt the keyword focus for each market's search behavior.
Automation enables maintenance: The real value isn't the initial generation—it's having alt text automatically created for every new product.
The biggest mistake I see teams make is treating this as a one-time project instead of an ongoing system. Alt text automation should be part of your product upload workflow, not a periodic cleanup task.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS companies, apply this automation approach to:
Feature screenshots and product demos
Integration logos and service images
Help documentation and tutorial images
Blog post illustrations and infographics
For your Ecommerce store
For ecommerce stores, prioritize automation for:
Product catalog images and variants
Category and collection thumbnails
Lifestyle and context images
Multi-language product descriptions