This workflow automatically generates on-brand, nostalgic Sims-style blog images from article content using AI-powered description generation, image creation, and virtual audience validation. It takes blog titles and content through a web form, creates contextually relevant image descriptions using GPT-4o, generates custom images with a distinctive pixelated aesthetic, validates them with Rally's AI personas, and iteratively improves until the audience approves - all while maintaining a complete audit trail in Google Sheets.
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Mike Taylor
Blog Image Generator
Mike Taylor
Generate contextually perfect blog images with nostalgic Sims-game aesthetics using AI content analysis, image generation, and Rally audience validation. Features automatic iteration and improvement until virtual personas approve the visual content.
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Overview
🎯 Pro Tips & Secret Sauce
The magic lies in the iterative AI-human feedback loop combined with distinctive visual branding:
- Content-Aware Image Descriptions - GPT-4o analyzes blog content to create contextually perfect image concepts that match the article's core message and tone
- Signature Visual Identity - Every image uses a distinctive "nostalgic Sims game" aesthetic with pixelated graphics, earthy tones, and atmospheric depth that creates instant brand recognition
- Virtual Audience Pre-Validation - Rally's AI personas test each image asking "Would you click on this image?" providing quantified engagement predictions before human review
- Smart Iteration System - If personas don't approve, the workflow automatically rewrites descriptions based on feedback and regenerates until it gets audience buy-in
- Complete Generation Tracking - Every iteration, feedback cycle, and final decision is logged to Google Sheets with URLs, descriptions, and audience scores for continuous improvement
This creates a fully automated visual content system that produces consistently on-brand images while ensuring they'll actually engage your target audience - like having a design team that never sleeps and always tests with focus groups.
📝 Step-by-Step Instructions
- Form Submission - User submits blog title and content through the web form interface
- Initialize Counter - Sets up iteration tracking starting at attempt #1 for the feedback loop system
- Generate Image Description - OpenAI GPT-4o analyzes the blog content and creates a detailed description of what the featured image should depict
- Format Description - Structures the AI-generated description for the image generation prompt
- Create Custom Image - Uses OpenAI DALL-E with a detailed nostalgic Sims-game aesthetic prompt to generate the blog image
- Upload to S3 - Saves the generated image to AWS S3 bucket with a filename based on blog title and workflow name
- Generate Public URL - Creates the public CDN URL for the uploaded image using URL-safe encoding
- Rally Audience Testing - Sends the image to Rally's AI personas asking "Would you click on this image?" for engagement prediction
- Split Persona Responses - Processes individual responses from each AI persona in the audience simulation
- Format Individual Response - Extracts and structures each persona's reaction for aggregation
- Aggregate All Responses - Combines all persona reactions into a comprehensive response dataset
- Check Image Relevance - Uses GPT-4o to analyze persona feedback and determine if the image is appropriate for the blog post
- Evaluate Approval - Checks if the image meets relevance standards and iteration count is reasonable
- Log Final Results - Records successful image details (title, description, feedback, URL) to Google Sheets for audit trail
- Increment Counter - If not approved, increases attempt counter for the next iteration
- Rewrite Description - Based on persona feedback, creates an improved image description addressing specific concerns
- Format Improved Description - Structures the revised description for another generation attempt
- Loop Back - Returns to image generation step with improved description until audience approval is achieved
📋 Requirements
Required Integrations
- OpenAI - GPT-4o for image description writing and relevance checking, plus DALL-E for image generation
- Rally API - AI persona validation service for testing image appeal with virtual audiences
- Google Sheets - Complete audit trail and iteration tracking
- AWS S3 - Cloud storage for generated images with public URL access
- Form Trigger - Web form interface for blog content input
Required Credentials
- OpenAI API key with GPT-4o and DALL-E image generation access
- Rally API Bearer token with persona audience access
- Google Sheets OAuth2 credentials with spreadsheet write permissions
- AWS S3 credentials with bucket upload permissions
- Public S3 bucket configured for image hosting
Setup Prerequisites
- Active Rally account with configured audience personas (default: rb842b547c27640)
- AWS S3 bucket named "n8n-workflows" with public read access
- Google Sheets document for logging image generation sessions
- n8n instance with form trigger capability enabled
- Cloudflare R2 or S3-compatible CDN for fast image delivery
🚀 n8n Workflow Template
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"content": "You are an art director for a professional publication, and your job is to describe what the featured image should look like when given a blog post. Be brief, and only return the details that are most essential to making an unforgettable image. Only return the description, no other text.",
"role": "system"
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{
"content": "=Briefly describe what image would make sense for the featured image on this blog post:\n\n# {{ $json[\u0027Blog Title\u0027] }}\n\n{{ $json[\u0027Blog Content\u0027] }}"
}
]
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"name": "Generate Image",
"parameters": {
"model": "gpt-image-1",
"options": {},
"prompt": "=A panoramic, pixelated scene in the nostalgic style of early Sims games from the 80s or 90s. The scene depicts {{ $json.description }} The scene is fully filled in with the correct earthy tones, ensuring a consistent, seamless look. The ground is properly colored to match the nostalgic game style, seamlessly blending with the overall pixelated aesthetic. The color palette is harmonious, soft, and slightly nostalgic, reminiscent of a classic simulation game: - **Earthy tones**: The background consists of shades of light brown, beige, and green, giving a natural, open-air feel. - **Deep greens**: The scattered trees or plants contrast with dark green hues, blending into the environment. - **Soft blues and teals**: The sky and distant hills or buildings incorporate cool blue and teal shades, adding atmospheric depth. - **Muted reds and yellows**: There are specific elements that feature vibrant yet faded tones, standing out vibrantly against the open landscape. - **Gradient sky**: The sky transitions from a light yellowish tone near the horizon to a soft blue-green hue, creating a dreamy and nostalgic morning aesthetic. The lighting is soft and diffused, avoiding harsh contrasts while maintaining clarity. Distant elements fade into a subtle atmospheric haze, adding natural depth. Shadows are blended gently into the ground and trees, matching the visual quirks of early game rendering. The landscape has visible pixelation, dithering effects, and scanline artifacts, reinforcing the low-resolution nostalgic aesthetic. Trees, buildings, and terrain are blocky and artificial, resembling early game assets. The sky is flatter, with pixelated clouds that are less detailed to maintain the vintage game feel. The image is wide and panoramic, evoking classic video game scenes, without any modern branding or elements.",
"resource": "image"
},
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"type": "@n8n/n8n-nodes-langchain.openAi",
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"name": "Save Image",
"parameters": {
"additionalFields": {},
"bucketName": "n8n-workflows",
"fileName": "={{ $(\u0027On form submission\u0027).item.json[\u0027Blog Title\u0027] }} - {{ $workflow.name }}",
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{
"id": "e7624485-6573-4087-a05b-839942e99526",
"name": "Get URL",
"parameters": {
"jsCode": "// Get your filename from previous node\nconst filename = $(\u0027On form submission\u0027).first().json[\u0027Blog Title\u0027] + \" - \" + $workflow.name\n\n// Make it URL-safe\nconst urlSafeFilename = encodeURIComponent(filename);\n\n// Build the full URL\nconst baseUrl = \"https://pub-id.r2.dev/\";\nconst fullUrl = baseUrl + urlSafeFilename;\n\nreturn {\n originalFilename: filename,\n urlSafeFilename: urlSafeFilename,\n fullUrl: fullUrl\n};",
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{
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"id": "6b97eeb9-c19c-4318-aad4-ab340a5cfa47",
"name": "Call Rally",
"parameters": {
"authentication": "genericCredentialType",
"bodyParameters": {
"parameters": [
{
"name": "smart",
"value": "false"
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{
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"value": "openai"
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"value": "=Would you click on this image?"
},
{
"name": "audience_id",
"value": "rb842b547c27640"
},
{
"name": "files",
"value": "={{ [{\"type\": \"image/png\", \"url\": $json.fullUrl}] }}"
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"name": "Check Relevance",
"parameters": {
"jsonOutput": true,
"messages": {
"values": [
{
"content": "=Do the user reactions to this blog image indicate that this is a good selection for the blog post \"{{ $(\u0027On form submission\u0027).item.json[\u0027Blog Title\u0027] }}\"? Respond in JSON with key option, value \"YES\", or \"NO\", and key feedback with brief instructions on what to change.\n\n{{ $json.responses }}"
}
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"operator": {
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"typeValidation": "strict",
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"name": "Rewrite Description",
"parameters": {
"messages": {
"values": [
{
"content": "You are an art director for a professional publication, and your job is to take some feedback from the users on a blog post, and give direction on how to improve the image for the blog post. Be brief, and only return the details that are most essential to making an unforgettable image. Only return the description, no other text.",
"role": "system"
},
{
"content": "=Previous Description:\n{{ $json.description }}\n\nFeedback on Previous Description:\n{{ $json.message.content.feedback }}\n\nBriefly describe what image would make sense for the featured image on this blog post:\n\n# {{ $(\u0027On form submission\u0027).item.json[\u0027Blog Title\u0027] }}\n\n{{ $(\u0027On form submission\u0027).item.json[\u0027Blog Content\u0027] }}"
}
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"assignments": [
{
"id": "76861df4-8aa5-4ddf-99ba-908ab20f9c6e",
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"assignments": [
{
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"name": "blog_title",
"type": "string",
"value": "={{ $(\u0027On form submission\u0027).item.json[\u0027Blog Title\u0027] }}"
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"id": "11fe07a1-7a88-4330-b40c-15b469992f1b",
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"type": "string",
"value": "={{ $json.message.content.feedback }}"
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{
"id": "56163736-4341-4971-a169-0948228e86e8",
"name": "image_url",
"type": "string",
"value": "={{ $(\u0027Get URL\u0027).item.json.fullUrl }}"
}
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"name": "Increment Counter",
"parameters": {
"jsonOutput": "={{ { \"counter\": $json.counter + 1 } }}",
"mode": "raw",
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{
"id": "7310a63e-b3e2-4ca1-afa5-3f4f7e305ed7",
"name": "Sticky Note",
"parameters": {
"color": 4,
"content": "## Generate an Image",
"height": 400,
"width": 820
},
"position": [
760,
-140
],
"type": "n8n-nodes-base.stickyNote",
"typeVersion": 1
},
{
"id": "167feb6a-923a-4db8-8171-fb88267bbbfd",
"name": "Sticky Note1",
"parameters": {
"content": "## Generate a Description",
"height": 560,
"width": 900
},
"position": [
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"type": "n8n-nodes-base.stickyNote",
"typeVersion": 1
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{
"id": "51073434-9e4f-44c4-b27e-c967a1836c46",
"name": "Sticky Note2",
"parameters": {
"color": 5,
"content": "## Ask Rally Audience",
"height": 560,
"width": 820
},
"position": [
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"type": "n8n-nodes-base.stickyNote",
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"pinData": {
"On form submission": [
{
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"Blog Content": "What if you could test product-market fit before building anything? This experiment shows AI personas can predict real user PMF responses with 88.5% accuracy.\r\n\r\nJune 16, 2025\r\n\u2190 Back to Articles\r\nProduct-market fit isn\u0027t just a milestone, it\u0027s an ongoing measurement in a constant state of flux. The tried and tested PMF survey gives a snap shot into whether people want what you\u0027ve put into their hands. What if you could run the same measurement in minutes, dare I say, without involving any users?\r\n\r\nThat sounded crazy typing it, but if you could predict a PMF missfire, you could skip building the thing nobody wants and focus instead on the thing the market has an appetite for. Get a new killer feature in the shower after you\u0027ve hit send on a real survey and the opportunity to ask is gone. In the real world, you can\u0027t use data that you did\u0027nt collect. But with an audience simulator, it\u0027s almost as good as having a time machine. Just spinup another crowd ready-to-roast and test your new value props. Rinse repeat until you have enough PMF in the virtual world to merit the build.\r\n\r\nRally has real users and we just ran a PMF survey. So I tested how well AI personas could predict the actual responses. First with an audience cloned from demo transcripts, then gave them \"memories\" of the survey data but witholding the actual PMF question/response. The AI personas knew things like how they explained Rally to others and the key benefit, but did\u0027nt know if they\u0027d be disappointed if they could no longer use Rally.\r\n\r\nThe idea being if this worked, you could try and run it for a product you\u0027ve not built yet by sourcing similar data for tangent product offerings. e.g. scraping review websites or even surveying the users of established players. With your enriched AI personas, you could then battle test your product concepts knowing the AI personas are grounded in real humans voices. \r\n\r\n Here\u0027s what happened. \r\nSetting the Benchmark With Real Humans\r\nThe responses to my survey are still coming in, but after 32 real human submisions, here\u0027s what they answered to \"How would you feel if you could no longer use Rally?\"\r\n\r\n- Very Disappointed: 15.6%\r\n- Somewhat Disappointed: 62.5%\r\n- Not Disappointed: 21.9%\r\n\r\nThe 40% \"Very Disappointed\" rule of thumb suggests you\u0027ve hit PMF when at least 4 in 10 users would be very disappointed to lose your product. Rally sits at 15.6%. Solid engagement, but clearly room to grow. The majority (62.5%) would be somewhat disappointed, indicating the product delivers value but isn\u0027t quite indispensable (yet).\r\n\r\nThis benchmark became my target: could AI personas replicate these exact percentages, synthetically?\r\n\r\nWhen testing with the enriched AI personas, I gave them memories of answeres to the other questions in the survey, just not the PMF question.\r\nWhat is the main benefit you receive from using Rally?\r\nHow would you explain Rally to a friend or colleague?\r\nWhat\u0027s stopping you from purchasing a paid subscription?\r\nHow could we double the value you get from Rally?\r\n\r\nHere\u0027s what that looked like when added to the persona background.\r\n\r\n\r\nDesigning Eight Synthetic Panels\r\nI tested the question across a few models, with and without memories (data enrichment) and tweaking the product description.\r\nModels tested:\r\n- Anthropic Claude (fast)\r\n- OpenAI GPT-4 (fast)\r\n- Google Gemini (fast)\r\n\r\nContext levels:\r\n- Generic personas: Basic demographic and psychographic profiles\r\n- Enriched personas: Loaded with real user memories of actual survey responses\r\n\r\nCopy tone:\r\n- Positive marketing blurb: e.g. \"Rally is used as a rapid fire replacement for running focus groups when...\"\r\n- Neutral factual description: e.g. \"Rally is a research platform that generates synthetic personas so you can...\"\r\n\r\nEach of the eight panels answered the identical PMF question. \r\n\r\nThe Results\r\n \r\n\r\nKey findings:\r\nThese results show that a product description with the positive-tone routinely over-predicted \"Very Disappointed\" (often \u003e90%). More neutral copy helped boost accuracy. Enriching with real survey data contributed to a big improvement to accuracy. The Anthropic/fast coming out on top, with an overall accuracy of 88.5%. Around 17 points off \"Very Disappointed\" and \"Somewhat Disappointed\" votes from real humans, and nearly perfect for \"Not Disappointed\". However, OpenAI was\u0027nt far behind, and a model you can use on our $20/m plan.\r\n\r\nThe key takeaway for me here being, generic personas (without enriched memories) consistently missed the mark by wider margins. Helping the case for running a strategic data enrichment if wanting to use synthetic audience simulators to pre-validate your product.\r\n\r\nWrapping up\r\nPMF isn\u0027t one-and-done measurement. Markets shift. Products evolve. Competition emerges. You need to constantly re-measure where you stand. Even though AI has made it easier / faster to build products, shipping them, aquiring users, and collecting PMF signal takes time. Now that I have an audience and simulator showing some promising PMF predictive signals, one test I\u0027d like to run next is simulate the many ideas that have been discussed internally and stress test our currenmt roadmap to see if the bets work in silicone. Then truth check with the humans. Every cycle sharpening a mental model of what really moves the needle.\r\n\r\nPMF measurement just got a lot faster. Use it wisely",
"Blog Title": "Can it Predict PMF?",
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"submittedAt": "2025-06-20T09:35:56.146-04:00"
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"content": "A futuristic split-screen image: on one side, a sleek AI brain formed from flowing data streams analyzing charts and survey results; on the other, real human faces reacting to a survey question on a digital interface. In the background, a glowing \u201c88.5%\u201d accuracy metric floats above intersecting lines symbolizing synthetic and real-world alignment. High-tech, clean aesthetic with a subtle color gradient emphasizing innovation and predictive power.",
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}About the Author
Mike Taylor
Mike Taylor is the CEO & Co-Founder of Rally. He previously co-founded a 50-person growth marketing agency called Ladder, created marketing & AI courses on LinkedIn, Vexpower, and Udemy taken by over 450,000 people, and published a book with O’Reilly on prompt engineering.
