Multi-Format Content Adaptation

One piece of content can serve many channels. This lesson covers workflows for adapting content across formats while maintaining consistency.

The Content Multiplication Model

                    ┌→ Blog Post
                    ├→ Social Media Posts
Original Content ──┼→ Email Newsletter
                    ├→ Video Script
                    └→ Podcast Notes

Adaptation Strategies

Core Message Extraction

First, identify what must stay consistent:

Format-Specific Adaptation

Each format has different requirements:

Format	Length	Tone	Focus
Blog	1500-2500 words	Educational	Depth
Twitter	280 chars	Punchy	Hook
LinkedIn	1300 chars	Professional	Insight
Email	300-500 words	Personal	Value
Video Script	500-1000 words	Conversational	Clarity

Format-Specific Chains

Social Media Adaptation

Email Newsletter Adaptation

async function adaptToNewsletter(content, subscriberSegment) {
  const prompt = `
Adapt this blog content into an email newsletter.

BLOG CONTENT:
${content.summary}

KEY POINTS:
${content.keyPoints.join('\n')}

SUBSCRIBER SEGMENT: ${subscriberSegment}

EMAIL REQUIREMENTS:
- Subject line (50 chars max, 30-40% open rate target)
- Preview text (90 chars max)
- Personal opening
- 3 key takeaways (bullet points)
- One clear CTA
- PS line with bonus content

Generate the complete email:
`;

  return await llm.chat({ content: prompt });
}

Video Script Adaptation

Adaptation Pipeline

Full Multi-Format Workflow

async function multiFormatAdaptation(originalContent) {
  // Extract core elements
  const core = await extractCoreMessage(originalContent);

  // Generate all formats in parallel
  const [twitter, linkedin, email, videoScript, podcastNotes] = await Promise.all([
    adaptToTwitter(core),
    adaptToLinkedIn(core),
    adaptToEmail(core),
    adaptToVideoScript(core),
    adaptToPodcastNotes(core)
  ]);

  // Verify consistency
  const consistency = await verifyConsistency({
    twitter, linkedin, email, videoScript, podcastNotes
  }, core);

  return {
    original: originalContent,
    core,
    adaptations: { twitter, linkedin, email, videoScript, podcastNotes },
    consistencyScore: consistency.score,
    warnings: consistency.warnings
  };
}

Consistency Verification

async function verifyConsistency(adaptations, core) {
  const checks = [];

  for (const [format, content] of Object.entries(adaptations)) {
    // Check core message preserved
    const messagePreserved = await checkMessagePresent(content, core.core_message);

    // Check key stats accurate
    const statsAccurate = await checkStatsAccuracy(content, core.key_statistics);

    // Check CTA aligned
    const ctaAligned = await checkCTAAlignment(content, core.call_to_action);

    checks.push({
      format,
      messagePreserved,
      statsAccurate,
      ctaAligned,
      score: (messagePreserved + statsAccurate + ctaAligned) / 3
    });
  }

  return {
    checks,
    score: checks.reduce((sum, c) => sum + c.score, 0) / checks.length,
    warnings: checks.filter(c => c.score < 0.8).map(c => `${c.format}: consistency issues`)
  };
}

Exercise: Build a Multi-Format Pipeline

Key Takeaways

• Extract core message before adapting
• Each format has specific constraints and goals
• Parallel generation improves efficiency
• Verify consistency across all adaptations
• Maintain key messages and statistics across formats
• Adapt tone and depth for each channel
• Build reusable adaptation templates

In the next module, we'll explore code generation workflows.