🤖 [AI-Powered] Automated content creation and distribution system

[jeevanpillay]

Description

Build an AI-powered system to automate content creation, optimization, and distribution for the marketing strategy.

Overview

Replace agency dependency with AI automation:

• Cost: $100-500/month (vs $7K+/month for agencies)
• Speed: Generate content in minutes vs weeks
• Scale: Create unlimited content on-demand
• Control: Full ownership and customization

AI Content Stack

1. Content Generation (Primary Tools)

Claude (Anthropic) - Primary Writer

Use for:

• Long-form blog posts (1,500-3,000 words)
• Technical tutorials with code examples
• API documentation
• Architecture explanations

Workflow:

# Use Claude API or Claude.ai with structured prompts
claude-api generate \
  --template "technical-blog-post" \
  --topic "Building Knowledge Agents with MCP" \
  --keywords "mcp, knowledge agent, team memory" \
  --style "technical, educational, code-heavy"

Cost: $15-20/million tokens (~200-300 articles)

GPT-4o (OpenAI) - Secondary Writer

Use for:

• Quick blog posts
• Social media content
• Email newsletters
• Product descriptions

Cost: $5-10/million tokens

Gemini 2.0 (Google) - Specialized Tasks

Use for:

• Code generation and examples
• Technical accuracy verification
• Multi-modal content (with images)

Cost: Free tier available

2. Content Optimization

SEO Optimization (AI-Powered)

Tools:

• Claude/GPT-4 for keyword optimization
• Surfer SEO ($89/month) - AI SEO analysis
• Frase.io ($45/month) - AI content briefs

Workflow:

// Automated SEO optimization pipeline
async function optimizeForSEO(content: string, keyword: string) {
  // 1. Analyze top-ranking content
  const topContent = await analyzeSerp(keyword);
  
  // 2. Generate optimized version
  const optimized = await claude.generate({
    prompt: \`Optimize this content for "\${keyword}":
    
    Current content: \${content}
    
    Top-ranking patterns: \${topContent}
    
    Requirements:
    - Include keyword in title, first 100 words, H2
    - Natural keyword density (1-2%)
    - LSI keywords: \${topContent.lsiKeywords}
    - Word count: \${topContent.avgWordCount}
    - Reading level: \${topContent.readingLevel}\`,
  });
  
  return optimized;
}

3. Code Example Generation

GitHub Copilot + Claude

Use for:

• Tutorial code examples
• Integration snippets
• Demo applications

Workflow:

// Generate working code examples
async function generateCodeExample(description: string) {
  const code = await claude.generate({
    prompt: \`Generate a complete, working code example:
    
    Task: \${description}
    
    Requirements:
    - TypeScript/Next.js 15
    - Include error handling
    - Add comments explaining key parts
    - Follow Lightfast code style
    - Include type definitions
    - Add testing examples\`,
  });
  
  // Validate code compiles
  await validateCode(code);
  
  return code;
}

4. Image Generation

Diagram Generation

Tools:

• Mermaid.js (free) - Architecture diagrams via code
• Excalidraw (free) - Hand-drawn style diagrams
• DALL-E 3 ($0.04/image) - Custom illustrations

Workflow:

// Generate diagrams from AI descriptions
async function generateDiagram(description: string) {
  const mermaidCode = await claude.generate({
    prompt: \`Create a Mermaid.js diagram for: \${description}
    
    Use appropriate diagram type (flowchart, sequence, architecture)\`,
  });
  
  return mermaidCode;
}

---

Automated Content Pipeline

Phase 1: Content Planning (AI-Assisted)

Tool: Custom AI Agent

// Automated content calendar generation
async function generateContentCalendar(weeks: number) {
  const calendar = await claude.generate({
    systemPrompt: \`You are a content strategist for Lightfast.
    
    Context:
    - Product: Team memory API
    - Audience: Developers, AI engineers
    - Keywords: \${seoKeywords}
    - Goals: SEO traffic, developer adoption\`,
    
    prompt: \`Generate a \${weeks}-week content calendar with:
    - 2 posts per week
    - Mix of tutorials (40%), product (30%), thought leadership (30%)
    - Each with: title, keyword, outline, CTAs
    - Optimized for search intent\`,
  });
  
  return calendar;
}

Phase 2: Content Generation (Fully Automated)

Workflow:

// End-to-end content generation
async function generateBlogPost(topic: ContentTopic) {
  // 1. Research phase
  const research = await Promise.all([
    searchWeb(topic.keyword),           // Exa search
    analyzeCompetitors(topic.keyword),  // SERP analysis
    getLatestTrends(topic.category),    // Industry trends
  ]);
  
  // 2. Outline generation
  const outline = await claude.generate({
    prompt: \`Create detailed outline for: "\${topic.title}"
    
    Research: \${research}
    Target keyword: \${topic.keyword}
    Word count: 2,000-2,500
    Include: intro, 5-7 main sections, code examples, conclusion\`,
  });
  
  // 3. Content generation (section by section)
  const sections = await Promise.all(
    outline.sections.map(section => 
      generateSection(section, research, topic)
    )
  );
  
  // 4. Code examples
  const codeExamples = await generateCodeExamples(topic.codeNeeds);
  
  // 5. Assembly
  const fullContent = assembleContent({
    sections,
    codeExamples,
    metadata: topic,
  });
  
  // 6. SEO optimization
  const optimized = await optimizeForSEO(fullContent, topic.keyword);
  
  // 7. Quality checks
  await runQualityChecks(optimized);
  
  return optimized;
}

Phase 3: Quality Control (AI + Human)

Automated Checks:

async function runQualityChecks(content: string) {
  const checks = await Promise.all([
    // Technical accuracy
    verifyTechnicalAccuracy(content),
    
    // Code compilation
    validateAllCodeExamples(content),
    
    // SEO optimization
    checkSEOScore(content),
    
    // Readability
    checkReadability(content),
    
    // Plagiarism
    checkOriginality(content),
    
    // Tone consistency
    checkBrandVoice(content),
  ]);
  
  return checks;
}

Human Review (15-30 min per post):

• Technical accuracy spot-check
• Brand voice alignment
• Code example testing
• Final approval

Phase 4: Publishing (Automated)

Workflow:

async function publishContent(content: BlogPost) {
  // 1. Generate metadata
  const metadata = await generateMetadata(content);
  
  // 2. Create MDX file
  await createMDXFile({
    path: \`apps/www/src/app/(app)/blog/\${content.slug}/page.mdx\`,
    content: content.body,
    frontmatter: {
      title: metadata.title,
      description: metadata.description,
      publishedAt: new Date().toISOString(),
      author: 'Lightfast Team',
      keywords: metadata.keywords,
      image: metadata.ogImage,
    },
  });
  
  // 3. Generate OG image
  const ogImage = await generateOGImage(content.title);
  
  // 4. Commit and deploy
  await git.commit(\`Add blog post: \${content.title}\`);
  await git.push();
  
  // 5. Trigger build (Vercel auto-deploys)
}

---

Automated Distribution

1. Social Media Automation

X/Twitter (Thread Generator)

async function generateTwitterThread(blogPost: BlogPost) {
  const thread = await claude.generate({
    prompt: \`Convert this blog post into a Twitter thread:
    
    \${blogPost.content}
    
    Requirements:
    - 8-12 tweets
    - Hook in first tweet
    - Key insights in middle
    - CTA in last tweet
    - Include code snippets where relevant
    - Use line breaks for readability\`,
  });
  
  // Auto-post via Twitter API
  await postThread(thread);
}

LinkedIn Post Generator

async function generateLinkedInPost(blogPost: BlogPost) {
  const post = await claude.generate({
    prompt: \`Convert this blog post into a LinkedIn post:
    
    \${blogPost.content}
    
    Style: Founder-led, technical but accessible
    Length: 1,000-1,500 characters
    Include: Hook, key insight, CTA
    Format: Short paragraphs, emojis sparingly\`,
  });
  
  return post;
}

2. Cross-Platform Publishing

Dev.to Auto-Publish

async function crossPostToDevTo(blogPost: BlogPost) {
  await devto.createArticle({
    title: blogPost.title,
    body_markdown: blogPost.content,
    published: true,
    canonical_url: \`https://lightfast.ai/blog/\${blogPost.slug}\`,
    tags: blogPost.tags.slice(0, 4),
  });
}

3. Newsletter Automation

Weekly Digest Generator

async function generateNewsletterDigest() {
  const recentPosts = await getRecentBlogPosts(7);
  
  const newsletter = await claude.generate({
    prompt: \`Create weekly newsletter from these blog posts:
    
    \${recentPosts}
    
    Sections:
    1. Featured article (main post)
    2. Quick tips (2-3)
    3. This week at Lightfast
    4. From around the web (3 curated links)
    
    Tone: Helpful, technical but friendly\`,
  });
  
  await resend.emails.send({
    from: 'Memory Lane <newsletter@lightfast.ai>',
    to: subscribers,
    subject: newsletter.subject,
    html: newsletter.html,
  });
}

---

AI Tools & Costs

Monthly Budget Breakdown

Content Generation:

• Claude Pro: $20/month (unlimited for planning)
• Claude API: $50/month (for automation)
• GPT-4 API: $20/month (backup)
• Subtotal: $90/month

SEO & Optimization:

• Surfer SEO: $89/month (optional)
• OR Frase.io: $45/month (budget option)
• Subtotal: $45-89/month

Images & Diagrams:

• DALL-E 3: $10/month (~250 images)
• Mermaid.js: Free
• Subtotal: $10/month

Automation:

• Zapier/Make: $30/month
• Subtotal: $30/month

Total: $175-219/month vs $7,000+/month (97% savings!)

---

Implementation Plan

Week 1: Setup

• Set up Claude/GPT-4 API keys
• Create prompt library (templates)
• Build automation scripts
• Set up quality control checklist

Week 2: Test Generation

• Generate 3 test blog posts
• Review and refine prompts
• Validate code examples
• Test publishing pipeline

Week 3: Automate Distribution

• Set up social media automation
• Configure cross-posting
• Test newsletter generation
• Create monitoring dashboard

Week 4: Production

• Generate 2 posts/week
• Auto-distribute across channels
• Track performance metrics
• Iterate on prompts

---

Prompt Library (Store in Repo)

Create `/prompts/` directory:
```
/prompts
├── blog-post.md # Main blog post template
├── tutorial.md # Step-by-step tutorial
├── comparison.md # Comparison article
├── technical-deep-dive.md # Architecture posts
├── twitter-thread.md # Twitter conversion
├── linkedin-post.md # LinkedIn conversion
├── newsletter.md # Newsletter digest
└── seo-optimize.md # SEO optimization
```

---

Success Metrics

Content Quality

• Technical accuracy: >95%
• SEO score (Lighthouse): >90
• Readability (Hemingway): Grade 8-10
• Plagiarism: 0%

Production Efficiency

• Time per post: 30-60 min (vs 8-12 hours manual)
• Posts per week: 2-3 (consistent)
• Cost per post: $5-10 (vs $500-1,000 agency)

Business Impact

• Organic traffic growth: Track weekly
• Time to first ranking: <4 weeks
• Conversion rate: Blog → Signup >5%

---

Acceptance Criteria

• ✅ AI content generation pipeline built
• ✅ 3 test posts generated and published
• ✅ Quality control process validated
• ✅ Auto-distribution working
• ✅ Cost <$250/month
• ✅ Producing 2+ posts/week consistently

Related

• Epic: 📋 [EPIC] Marketing & Distribution Strategy - Complete Plan #312
• Replaces: Manual content creation in Phase 1-2

[jeevanpillay]

📊 Training Data Strategy for AI Content Generation

Available Datasets & Resources

Public Research on High-Performing Content

1. Backlinko's 11.8M Search Results Dataset

   • GitHub: https://github.com/Backlinko-LLC/search-engine-ranking
   • Insights: Word count, title optimization, H1 tags, content scores

2. Animalz SaaS Benchmark Report

   • 150M pageviews from top SaaS blogs
   • Key finding: Tutorial content = most organic traffic
   • Top blogs have 6+ posts with 100K+ yearly views

3. SEMrush Blogging Stats

   • Top posts average 1,152 words
   • 62% are easy to read
   • "How-to" and "Comparison" posts perform best

Recommended Approach: Curate Your Own Dataset

Since no perfect "developer blog" dataset exists, scrape from top performers:

Top Sources to Learn From:

• Stripe Blog (https://stripe.com/blog)
• Vercel Blog (https://vercel.com/blog)
• GitHub Blog (https://github.blog)
• DigitalOcean Tutorials
• Anthropic Blog
• Cloudflare Blog
• Railway Blog
• Supabase Blog

Implementation Options

Option 1: Few-Shot Learning (Fastest)

Curate 10-20 excellent examples → Use as context in AI prompts

const examples = loadReferenceArticles(topic);
const prompt = `Here are 3 high-performing examples:\n${examples}\n\nNow generate: ${topic}`;

Option 2: RAG with Exa Search (Best)

Use Exa to find high-performers in real-time per topic:

const research = await exaSearch(`best ${topic} technical blog posts`);
const content = await generateWithResearch(research, topic);

Option 3: Build Custom Dataset (Most Control)

Scrape 50-100 top articles → Analyze patterns → Generate from patterns

Script available in comments above.

Quick Start (Recommended)

Week 1: Manually curate 10-15 top articles as examples
Week 2: Use Exa for real-time research per topic
Week 3: Iterate based on performance

Cost: ~$20-30/month (Exa + Claude API)

Next Steps

• Curate 10-15 reference articles
• Add to /data/reference-articles/ directory
• Update prompt templates to include examples
• Test generation quality with examples vs without
• Implement Exa-based RAG for continuous learning

Note: Using high-quality examples as context dramatically improves AI output quality vs zero-shot generation.