Create AI-Powered Content Analysis

Create AI-Powered Content Analysis

What You’ll Learn

Build intelligent workflows that can read, understand, and analyze web content like a human researcher. Perfect for content research, competitive analysis, and automated insights.

⏱️ Time: 45 minutes 🎯 Difficulty: Intermediate ✅ Result: AI workflow that analyzes content and generates insights

Why AI Content Analysis?

• Scale Human Intelligence: Analyze hundreds of articles in minutes
• Extract Insights: Find patterns and themes across large content sets
• Automate Research: Generate summaries, sentiment analysis, and key points
• Competitive Intelligence: Understand competitor messaging and positioning

The AI Analysis Pipeline

flowchart TD
    A[🌐 Web Content] --> B[📝 Extract Text]
    B --> C[✂️ Split into Chunks]
    C --> D[🤖 AI Analysis]
    D --> E[📊 Generate Insights]
    E --> F[📋 Structured Report]

    style A fill:#e3f2fd
    style B fill:#e8f5e8
    style C fill:#fff3e0
    style D fill:#f3e5f5
    style E fill:#f9fbe7
    style F fill:#fce4ec

Step 1: Set Up Content Extraction

Extract Clean Text Content

Start with reliable text extraction that feeds clean data to AI:

Get All Text From Link Configuration:

{
  "waitForLoad": true,
  "timeout": 25000,
  "textFilters": [
    ".navigation",
    ".footer",
    ".sidebar",
    ".advertisement",
    ".comments",
    ".related-articles"
  ],
  "includeMetadata": true
}

✅ You should see: Clean article text without navigation clutter

Handle Long Content

For articles longer than AI context limits, use text splitting:

Recursive Character Text Splitter:

{
  "chunkSize": 2000,
  "chunkOverlap": 200,
  "separators": ["\n\n", "\n", ". ", " "],
  "keepSeparator": true
}

Step 2: Design Your Analysis Prompts

Content Analysis Prompt Template

Create focused prompts that extract specific insights:

{
  "analysis_prompt": "Analyze this content and provide:\n\n1. **Main Topic**: What is this content primarily about?\n2. **Key Points**: List 3-5 most important points\n3. **Sentiment**: Overall tone (positive/negative/neutral)\n4. **Target Audience**: Who is this written for?\n5. **Content Quality**: Rate 1-10 with brief explanation\n\nContent to analyze:\n{content}\n\nProvide structured analysis:"
}

Specialized Analysis Types

Competitive Analysis:

{
  "competitive_prompt": "Analyze this competitor content for:\n\n1. **Value Proposition**: How do they position their solution?\n2. **Key Features**: What capabilities do they highlight?\n3. **Pricing Strategy**: Any pricing information or approach?\n4. **Target Market**: Who are they targeting?\n5. **Messaging Tone**: Professional, casual, technical, etc.\n\nContent: {content}"
}

SEO Content Analysis:

{
  "seo_prompt": "Analyze this content for SEO effectiveness:\n\n1. **Primary Keywords**: Main topics and terms\n2. **Content Structure**: Headings, organization, readability\n3. **User Intent**: What question does this answer?\n4. **Content Gaps**: What's missing or could be improved?\n5. **Optimization Score**: Rate 1-10 with suggestions\n\nContent: {content}"
}

Step 3: Build the Analysis Workflow

Core Workflow Nodes

Set up this proven 5-node analysis pattern:

1. Get All Text From Link - Extract content
2. Recursive Character Text Splitter - Handle long content
3. Basic LLM Chain - AI analysis
4. Edit Fields - Structure results
5. Download as File - Save insights

LLM Chain Configuration

Basic LLM Chain Setup:

{
  "llm": {
    "model": "gpt-3.5-turbo",
    "temperature": 0.3,
    "maxTokens": 1000
  },
  "prompt": "Analyze this content and provide structured insights:\n\n{content}\n\nAnalysis:",
  "outputParser": "structured"
}

✅ You should see: Structured AI analysis of your content

Step 4: Structure and Format Results

Extract Structured Data

Use Edit Fields to parse AI responses into structured data:

{
  "operations": [
    {
      "field": "ai_response",
      "operation": "extract_regex",
      "pattern": "Main Topic: ([^\\n]+)",
      "output_field": "main_topic"
    },
    {
      "field": "ai_response",
      "operation": "extract_regex",
      "pattern": "Sentiment: ([^\\n]+)",
      "output_field": "sentiment"
    },
    {
      "field": "ai_response",
      "operation": "extract_regex",
      "pattern": "Content Quality: ([0-9]+)",
      "output_field": "quality_score"
    }
  ]
}

Create Analysis Reports

CSV Output Format:

URL,Title,Main_Topic,Sentiment,Quality_Score,Key_Points,Analysis_Date
https://example.com/article1,Article Title,Marketing Strategy,Positive,8,"Point 1, Point 2, Point 3",2024-01-15

Real-World Examples

Example 1: Blog Content Analysis

Use Case: Analyze competitor blog posts for content strategy

Workflow Setup:

{
  "content_sources": [
    "https://competitor.com/blog/post1",
    "https://competitor.com/blog/post2"
  ],
  "analysis_focus": "content_strategy",
  "output_format": "competitive_intelligence_report"
}

Analysis Prompt:

Analyze this blog post for content strategy insights:

1. **Content Type**: Tutorial, thought leadership, product announcement, etc.
2. **Expertise Level**: Beginner, intermediate, advanced
3. **Call-to-Action**: What action do they want readers to take?
4. **Content Pillars**: What themes/topics do they focus on?
5. **Engagement Factors**: What makes this content engaging?

Content: {content}

Expected Output:

{
  "content_type": "Tutorial",
  "expertise_level": "Intermediate",
  "cta": "Sign up for free trial",
  "content_pillars": ["Automation", "Productivity", "Business Growth"],
  "engagement_factors": ["Step-by-step examples", "Real case studies", "Actionable tips"]
}

Example 2: Product Review Analysis

Use Case: Analyze product reviews to understand customer sentiment

Multi-Review Processing:

{
  "review_sources": [
    "https://reviews.com/product-a",
    "https://reviews.com/product-b"
  ],
  "analysis_type": "sentiment_and_features",
  "batch_size": 10
}

Review Analysis Prompt:

Analyze these product reviews:

1. **Overall Sentiment**: Positive, negative, or mixed
2. **Top Praised Features**: What do customers love?
3. **Common Complaints**: What issues come up repeatedly?
4. **Purchase Drivers**: Why do people buy this product?
5. **Improvement Suggestions**: What would make it better?

Reviews: {content}

Advanced AI Techniques

Multi-Step Analysis

For complex analysis, chain multiple AI calls:

flowchart LR
    A[Raw Content] --> B[Summary AI]
    B --> C[Sentiment AI]
    C --> D[Keyword AI]
    D --> E[Final Report]

    style A fill:#e3f2fd
    style B fill:#e8f5e8
    style C fill:#fff3e0
    style D fill:#f3e5f5
    style E fill:#fce4ec

Step 1 - Summarization:

{
  "prompt": "Summarize this content in 2-3 sentences, focusing on the main message and key points:\n\n{content}"
}

Step 2 - Sentiment Analysis:

{
  "prompt": "Analyze the sentiment of this summary. Provide:\n1. Overall sentiment (positive/negative/neutral)\n2. Confidence level (1-10)\n3. Key emotional indicators\n\nSummary: {summary}"
}

RAG-Enhanced Analysis

Use RAG (Retrieval-Augmented Generation) for context-aware analysis:

RAG Node Configuration:

{
  "vectorStore": "company_knowledge_base",
  "retrievalCount": 3,
  "prompt": "Based on our company knowledge and this content, analyze:\n\n1. How does this compare to our approach?\n2. What opportunities does this reveal?\n3. What threats should we be aware of?\n\nContent: {content}\nContext: {retrieved_context}"
}

Troubleshooting Guide

Top 5 Analysis Issues

Issue	Symptoms	Solution
Inconsistent AI responses	Results vary between runs	Lower temperature, add examples to prompt
Analysis too generic	Vague, unhelpful insights	Make prompts more specific, add constraints
Content too long for AI	Truncated or failed analysis	Use text splitter, analyze in chunks
Poor quality extraction	AI analyzing navigation/ads	Improve text filters, clean content first
Slow processing	Long wait times	Optimize prompts, use faster models, batch process

Debugging Workflow

1. Test with single article - Verify analysis quality
2. Check extracted text - Ensure clean content input
3. Refine prompts iteratively - Start simple, add complexity
4. Validate AI responses - Check for expected format
5. Monitor token usage - Optimize for cost and speed

Data Flow Examples

Simple Content Analysis

flowchart LR
    A[Article URL] --> B[Extract Text]
    B --> C[AI Analysis]
    C --> D[Structure Results]
    D --> E[CSV Report]

    style A fill:#e3f2fd
    style B fill:#e8f5e8
    style C fill:#fff3e0
    style D fill:#f3e5f5
    style E fill:#fce4ec

Batch Content Processing

flowchart TD
    A[URL List] --> B[Extract All Text]
    B --> C[Split Long Content]
    C --> D[Parallel AI Analysis]
    D --> E[Merge Results]
    E --> F[Generate Report]

    style A fill:#e3f2fd
    style D fill:#fff3e0
    style F fill:#fce4ec

Best Practices

Prompt Engineering

• Be specific: Ask for exactly what you need
• Provide examples: Show the AI your desired output format
• Set constraints: Limit response length and format
• Test iteratively: Refine prompts based on results

Performance Optimization

• Batch processing: Analyze multiple pieces together when possible
• Smart chunking: Split content at natural boundaries
• Cache results: Avoid re-analyzing unchanged content
• Choose appropriate models: Balance cost, speed, and quality

Quality Assurance

• Validate outputs: Check AI responses for accuracy
• Human review: Sample check important analyses
• Version control: Track prompt changes and results
• Error handling: Plan for AI failures and edge cases

What’s Next?

Expand your AI analysis capabilities:

• Extract Data from Any Website - Master content extraction first
• Build a Price Monitoring System - Combine with monitoring
• Advanced AI Workflows - Explore complex AI patterns

Complete Workflow Template

{
  "workflow": {
    "name": "AI Content Analyzer",
    "description": "Intelligent content analysis with structured insights",
    "nodes": [
      {
        "type": "LambdaInput",
        "config": {
          "urls": ["https://example.com/article1", "https://example.com/article2"]
        }
      },
      {
        "type": "GetAllTextFromLink",
        "config": {
          "waitForLoad": true,
          "timeout": 25000,
          "textFilters": [".navigation", ".footer", ".ads"],
          "includeMetadata": true
        }
      },
      {
        "type": "RecursiveCharacterTextSplitter",
        "config": {
          "chunkSize": 2000,
          "chunkOverlap": 200,
          "separators": ["\n\n", "\n", ". "]
        }
      },
      {
        "type": "BasicLLMChain",
        "config": {
          "llm": {
            "model": "gpt-3.5-turbo",
            "temperature": 0.3,
            "maxTokens": 1000
          },
          "prompt": "Analyze this content and provide:\n\n1. **Main Topic**: Primary subject\n2. **Key Points**: 3-5 important points\n3. **Sentiment**: Overall tone\n4. **Quality**: Rate 1-10\n\nContent: {content}\n\nAnalysis:"
        }
      },
      {
        "type": "EditFields",
        "config": {
          "operations": [
            {
              "field": "ai_response",
              "operation": "extract_regex",
              "pattern": "Main Topic: ([^\\n]+)",
              "output_field": "main_topic"
            },
            {
              "field": "ai_response",
              "operation": "extract_regex",
              "pattern": "Sentiment: ([^\\n]+)",
              "output_field": "sentiment"
            }
          ]
        }
      },
      {
        "type": "DownloadAsFile",
        "config": {
          "format": "csv",
          "filename": "content_analysis_{{timestamp}}.csv"
        }
      }
    ]
  }
}

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💡 Pro Tip: Start with simple analysis prompts and gradually add complexity. The key to good AI analysis is asking the right questions, not using the most advanced models.