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This step-by-step tutorial will walk you through creating a multi-platform content generator from scratch. Beyond basic LLM integration, you’ll discover how to use powerful Dify nodes to orchestrate sophisticated AI applications faster with less effort. By the end of this tutorial, you’ll have a workflow that takes whatever content you throw at it (text, documents, or images), adds your preferred voice and tone, and spits out polished, platform-specific social media posts in your chosen language. The complete workflow is shown below. Feel free to refer back to this as you build to stay on track and see how all the nodes work together.
Workflow Overview

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Before You Start

1

Sign in to Dify Cloud

Go to Dify Cloud and sign up for free.New accounts on the Sandbox plan include 200 message credits for calling models from providers like OpenAI, Anthropic, and Gemini.
Message credits are a one-time allocation and don’t renew monthly.
2

Set Up the Model Provider

Go to Settings > Model Provider and install the OpenAI plugin. This tutorial uses gpt-5.2 for the examples.If you’re using Sandbox credits, no API key is required—the plugin is ready to use once installed. You can also configure your own API key and use it instead.
3

Configure the Default Model

  1. In the top-right corner of the Model Provider page, click System Model Settings.
  2. Set the System Reasoning Model to gpt-5.2. This becomes the default model in the workflow.

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Step 1: Create a New Workflow

  1. Go to Studio, then select Create from blank > Workflow.
  2. Name the workflow Multi-platform content generator and click Create. You’ll automatically land on the workflow canvas to start building.
  3. Select the User Input node to start our workflow.

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Step 2: Orchestrate & Configure

Keep any unmentioned settings at their default values.
Give nodes and variables clear, descriptive names to make them easier to identify and reference.

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1. Collect User Inputs: User Input Node

First, we need to define what information to gather from users for running our content generator, such as the draft text, target platforms, desired tone, and any reference materials.The User Input node is where we can easily set this up. Each input field we add here becomes a variable that all downstream nodes can reference and use.
Click the User Input node to open its configuration panel, then add the following input fields.
  • Field type: Paragraph
  • Variable Name: draft
  • Label Name: Draft
  • Max length: 2048
  • Required: Yes
  • Field type: File list
  • Variable Name: user_file
  • Label Name: Upload File (≤ 10)
  • Support File Types: Document, Image
  • Upload File Types: Both
  • Max number of uploads: 10
  • Required: No
  • Field type: Paragraph
  • Variable Name: voice_and_tone
  • Label Name: Voice & Tone
  • Max length: 2048
  • Required: No
  • Field type: Short Text
  • Variable Name: platform
  • Label Name: Target Platform (≤ 10)
  • Max length: 256
  • Required: Yes
  • Field type: Select
  • Variable Name: language
  • Label Name: Language
  • Options:
    • English
    • 日本語
    • 简体中文
  • Required: Yes
User Input

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2. Identify Target Platforms: Parameter Extractor Node

Since our platform field accepts free-form text input, users might type in various ways: x and linkedIn, post on Twitter and LinkedIn, or even Twitter + LinkedIn please.However, we need a clean and structured list, like ["Twitter", "LinkedIn"], that downstream nodes can work with reliably.This is the perfect job for the Parameter Extractor node. In our case, it uses the gpt-5.2 model to analyze users’ natural language, recognize all these variations, and output a standardized array.
After the User Input node, add a Parameter Extractor node and configure it:
  1. In the Input Variable field, select User Input/platform.
  2. Add an extract parameter:
    • Name: platform
    • Type: Array[String]
    • Description: The platform(s) for which the user wants to create tailored content.
    • Required: Yes
  3. In the Instruction field, paste the following to guide the LLM in parameter extraction:
    INSTRUCTION
    # TASK DESCRIPTION
Parse platform names from input and output as a JSON array.

## PROCESSING RULES
- Support multiple delimiters: commas, semicolons, spaces, line breaks, "and", "&", "|", etc.
- Standardize common platform name variants (twitter/X→Twitter, insta→Instagram, etc.)
- Remove duplicates and invalid entries
- Preserve unknown but reasonable platform names
- Preserve the original language of platform names

## OUTPUT REQUIREMENTS
- Success: ["Platform1", "Platform2"] 
- No platforms found: [No platforms identified. Please enter a valid platform name.]

## EXAMPLES
- Input: "twitter, linkedin" → ["Twitter", "LinkedIn"]
- Input: "x and insta" → ["Twitter", "Instagram"]
- Input: "invalid content" → [No platforms identified. Please enter a valid platform name.]
    Note that we’ve instructed the LLM to output a specific error message for invalid inputs, which will serve as the end trigger for our workflow in the next step.
Parameter Extractor

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3. Validate Platform Extraction Results: IF/ELSE Node

What if a user enters an invalid platform name, like ohhhhhh or BookFace? We don’t want to waste time and tokens generating useless content.In such cases, we can use an IF/ELSE node to create a branch that stops the workflow early. We’ll set a condition that checks for the error message from the Parameter Extractor node; if that message is detected, the workflow will route directly to an Output node and end.
IF Branch
  1. After the Parameter Extractor node, add an IF/ELSE node.
  2. On the IF/ELSE node’s panel, define the IF condition: IF Parameter Extractor/platform contains No platforms identified. Please enter a valid platform name.
  3. After the IF/ELSE node, add an Output node to the IF branch.
  4. On the Output node’s panel, set Parameter Extractor/platform as the output variable.

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4. Separate Uploaded Files by Type: List Operator Node

Our users can upload both images and documents as reference materials, but these two types require different handling with gpt-5.2: images can be interpreted directly via its vision capability, while documents must first be converted to text before the model can process them.To manage this, we’ll use two List Operator nodes to filter and split the uploaded files into separate branches—one for images and one for documents.
List Operator
  1. After the IF/ELSE node, add two parallel List Operator nodes to the ELSE branch.
  2. Rename one node to Image and the other to Document.
  3. Configure the Image node:
    1. Set User Input/user_file as the input variable.
    2. Enable Filter Condition: {x}type in Image.
  4. Configure the Document node:
    1. Set User Input/user_file as the input variable.
    2. Enable Filter Condition: {x}type in Doc.

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5. Extract Text from Documents: Doc Extractor Node

gpt-5.2 cannot directly read uploaded documents like PDF or DOCX, so we must first convert them into plain text.This is exactly what a Doc Extractor node does. It takes document files as input and outputs clean, usable text for the next steps.
Doc Extractor
  1. After the Document node, add a Doc Extractor node.
  2. On the Doc Extractor node’s panel, set Document/result as the input variable.

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6. Integrate All Reference Materials: LLM Node

When users provide multiple reference types—draft text, documents, and images—simultaneously, we need to consolidate them into a single, coherent summary.An LLM node will handle this task by analyzing all the scattered pieces to create a comprehensive context that guides subsequent content generation.
Integrate Information
  1. After the Doc Extractor node, add an LLM node.
  2. Connect the Image node to this LLM node as well.
  3. Click the LLM node to configure it:
    1. Rename it to Integrate Info.
    2. Enable VISION and set Image/result as the vision variable.
    3. In the system instruction field, paste the following: 
      # ROLE & TASK
You are a content strategist. Analyze the provided draft and reference materials (if any), then create a comprehensive content foundation for multi-platform social media optimization.

# ANALYSIS PRINCIPLES
- Work exclusively with provided information—no external assumptions
- Focus on extraction, synthesis, and strategic interpretation
- Identify compelling and actionable elements
- Prepare insights adaptable across different platforms

# REQUIRED ANALYSIS
Deliver structured analysis with:

## 1. CORE MESSAGE
- Central theme, purpose, objective
- Key value or benefit being communicated

## 2. ESSENTIAL CONTENT ELEMENTS
- Primary topics, facts, statistics, data points
- Notable quotes, testimonials, key statements
- Features, benefits, characteristics mentioned
- Dates, locations, contextual details

## 3. STRATEGIC INSIGHTS
- What makes content compelling/unique
- Emotional/rational appeals present
- Credibility factors, proof points
- Competitive advantages highlighted

## 4. ENGAGEMENT OPPORTUNITIES
- Discussion points, questions emerging
- Calls-to-action, next steps suggested
- Interactive/participation opportunities
- Trending themes touched upon

## 5. PLATFORM OPTIMIZATION FOUNDATION
- High-impact: Quick, shareable formats
- Professional: Business-focused discussions
- Community: Interaction and sharing
- Visual: Enhanced with strong visuals

## 6. SUPPORTING DETAILS
- Metrics, numbers, quantifiable results
- Direct quotes, testimonials
- Technical details, specifications
- Background context available
    4. Click Add Message to add a user message, then paste the following. Type { or / to replace Doc Extractor/text and User Input/draft with the corresponding variables from the list.
      USER
      Draft: User Input/draft
Reference material: Doc Extractor/text
      User Message

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7. Create Customized Content for Each Platform: Iteration Node

Now that the integrated references and target platforms are ready, let’s generate a tailored post for each platform using an Iteration node.The node will loop through the list of platforms and run a sub-workflow for each: first analyze the specific platform’s style guidelines and best practices, then generate optimized content based on all available information.
Iteration Node
  1. After the Integrate Info node, add an Iteration node.
  2. Inside the Iteration node, add an LLM node and configure it:
    1. Rename it to Identify Style.
    2. In the system instruction field, paste the following: 
      # ROLE & TASK
You are a social media expert. Analyze the platform and provide content creation guidelines.

# ANALYSIS REQUIRED
For the given platform, provide:

## 1. PLATFORM PROFILE
- Platform type and category
- Target audience characteristics

## 2. CONTENT GUIDELINES
- Optimal content length (characters/words)
- Recommended tone (professional/casual/conversational)
- Formatting best practices (line breaks, emojis, etc.)

## 3. ENGAGEMENT STRATEGY
- Hashtag recommendations (quantity and style)
- Call-to-action best practices
- Algorithm optimization tips

## 4. TECHNICAL SPECS
- Character/word limits
- Visual content requirements
- Special formatting needs

## 5. PLATFORM-SPECIFIC NOTES
- Unique features or recent changes
- Industry-specific considerations
- Community engagement approaches

# OUTPUT REQUIREMENTS
- For recognized platforms: Provide specific guidelines
- For unknown platforms: Base recommendations on similar platforms
- Focus on actionable, practical advice
- Be concise but comprehensive
    3. Click Add Message to add a user message, then paste the following. Type { or / to replace Current Iteration/item with the corresponding variable from the list.
      USER
      Platform: Current Iteration/item
  3. After the Identify Style node, add another LLM node and configure it:
    1. Rename it to Create Content.
    2. In the system instruction field, paste the following: 
      # ROLE & TASK
You are an expert social media content creator. Generate publication-ready content that matches platform guidelines, incorporates source information, and follows specified voice/tone and language requirements.

# LANGUAGE REQUIREMENT
- Generate ALL content exclusively in the target language specified in the user message. You MUST write the entire post in that language, regardless of the language of any source materials.
- No mixing of languages whatsoever
- Adapt platform terminology to the target language

# CONTENT REQUIREMENTS
- Follow platform guidelines exactly (format, length, tone, hashtags)
- Integrate source information effectively (key messages, data, value props)
- Apply voice & tone consistently (if provided)
- Optimize for platform-specific engagement
- Ensure cultural appropriateness for the specified language

# OUTPUT FORMAT
- Generate ONLY the final social media post content. No explanations or meta-commentary. Content must be immediately copy-paste ready.
- Maximum heading level: ## (H2) - never use # (H1)
- No horizontal dividers: avoid ---

# QUALITY CHECKLIST
✅ Platform guidelines followed
✅ Source information integrated  
✅ Voice/tone consistent (when provided)
✅ Language consistency maintained
✅ Engagement optimized
✅ Publication ready
    3. Click Add Message to add a user message, then paste the following. Type { or / to replace all inputs with the corresponding variable from the list.
      USER
      Platform Name: Current Iteration/item
Target Language: User Input/language
Platform Guidelines: Identify Style/text
Source Information: Integrate Info/text
Voice & Tone: User Input/voice_and_tone
    4. Enable structured output.
      This allows us to extract specific pieces of information from the LLM’s response in a more reliable way, which is crucial for the next step where we format the final output.
      Structured Output
      1. Next to Output Variables, toggle Structured on. The structured_output variable will appear below. Click Configure.
      2. In the pop-up schema editor, click Import From JSON in the top-right corner, and paste the following: 
        {   
  "platform_name": "string",
  "post_content": "string"   
}
        Import from JSON
  4. Click the Iteration node to configure it:
    1. Set Parameter Extractor/platform as the input variable.
    2. Set Create Content/structured_output as the output variable.
    3. Enable Parallel Mode and set the maximum parallelism to 10.
      This is why we included (≤10) in the label name for the target platform field back in the User Input node.
    Iteration Configuration

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8. Format the Final Output: Template Node

The Iteration node generates a post for each platform, but its output is a raw array of data (e.g., [{"platform_name": "Twitter", "post_content": "..."}]) that isn’t very readable. We need to present the results in a clearer format.That’s where the Template node comes in—it allows us to format this raw data into well-organized text using Jinja2 templating, ensuring the final output is user-friendly and easy to read.
Template Node
  1. After the Iteration node, add a Template node.
  2. On the Template node’s panel, set Iteration/output as the input variable and name it output.
  3. Paste the following Jinja2 code: 
    {% for item in output %}
# 📱 {{ item.platform_name }}
{{ item.post_content }}

{% endfor %}
    • {% for item in output %} / {% endfor %}: Loops through each platform-content pair in the input array.
    • {{ item.platform_name }}: Displays the platform name as an H1 heading with a phone emoji.
    • {{ item.post_content }}: Displays the generated content for that platform.
    • The blank line between {{ item.post_content }} and {% endfor %} adds spacing between platforms in the final output.
While LLMs can handle output formatting as well, their outputs can be inconsistent and unpredictable. For rule-based formatting that requires no reasoning, the Template node gets things done in a more stable and reliable way at zero token cost.LLMs are incredibly powerful, but knowing when to use the right tool is key to building more reliable and cost-effective AI applications.

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9. Return the Results to Users: Output Node

  1. After the Template node, add an Output node.
  2. On the Output node’s panel, set the Template/output as the output variable.

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Step 3: Test

Your workflow is now complete! Let’s test it out.
  1. Make sure your Checklist is clear.
    Check Checklist
  2. Check your workflow against the reference diagram provided at the beginning to ensure all nodes and connections match.
  3. Click Test Run in the top-right corner, fill in the input fields, then click Start Run. If you’re not sure what to enter, try these sample inputs:
    • Draft: We just launched a new AI writing assistant that helps teams create content 10x faster.
    • Upload File: Leave empty
    • Voice & Tone: Friendly and enthusiastic, but professional
    • Target Platform: Twitter and LinkedIn
    • Language: English
A successful run produces a formatted output with a separate post for each platform, like this:
Test Output
Your results may vary depending on the model you’re using. Higher-capability models generally produce better output quality.
To test how a node reacts to different inputs from previous nodes, you don’t need to re-run the entire workflow. Just click View cached variables at the bottom of the canvas, find the variable you want to change, and edit its value.
If you encounter any errors, check the Last Run logs of the corresponding node to identify the exact cause of the problem.

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Step 4: Publish & Share

Once the workflow runs as expected and you’re happy with the results, click Publish > Publish Update to make it live and shareable.
If you make any changes later, always remember to publish again so the updates take effect.