Build an AI Content Pipeline with n8n: From RSS to Blog Post

One of our most-used internal workflows at Vorlux AI is the Content Pipeline — an n8n workflow that monitors RSS feeds, summarizes articles with a local LLM, generates blog post drafts, and queues them for review. Here’s how to build your own.

What You’ll Build

A workflow that:

1. Monitors 3-5 RSS feeds for AI industry news
2. Filters for relevance using keyword matching
3. Summarizes each article using Ollama (local LLM)
4. Generates a blog post draft with SEO metadata
5. Saves to a Google Sheet or Notion database for review

Estimated API cost: EUR 0 — everything runs locally.

What You’ll Build: The End Result

Before diving into the setup, here is exactly what the finished pipeline produces every day, without any manual effort:

• Automated monitoring of 3-5 RSS feeds, polling every 30 minutes for fresh AI industry news
• Smart filtering that discards irrelevant articles using keyword matching, so only on-topic content enters your pipeline
• AI-generated summaries of each article in 3 concise bullet points, written for a business audience
• Complete blog post drafts of 300+ words with practical takeaways, ready for human review and light editing
• SEO metadata including suggested title, tags, and meta description for each draft
• Organized review queue in Google Sheets or Notion, with status tracking (Draft / Reviewed / Published)
• Full audit trail showing the original source URL for every generated piece, ensuring proper attribution

In practice, this pipeline generates 5-15 draft blog posts per week depending on how many feeds you monitor and how broad your keywords are. A single human reviewer can process the entire weekly batch in under 30 minutes, turning raw AI output into publish-ready content. That replaces approximately 8-12 hours of manual research, reading, summarizing, and writing per week.

flowchart LR
    RSS["📡 RSS Feeds"] --> FILTER["🔍 Keyword<br/>Filter"]
    FILTER --> OLLAMA["🤖 Ollama<br/>Summarize"]
    OLLAMA --> FORMAT["📝 Format<br/>Draft"]
    FORMAT --> SHEETS["📊 Google Sheets<br/>Review Queue"]
    SHEETS --> PUBLISH["🚀 Publish"]
    
    style RSS fill:#F5A623,color:#0B1628
    style OLLAMA fill:#059669,color:#FAFAFA
    style PUBLISH fill:#059669,color:#FAFAFA

Prerequisites

• n8n installed (self-hosted or cloud)
• Ollama running locally with llama3.1:8b or similar
• RSS feed URLs for your target sources

Step 1: RSS Feed Trigger

Create an RSS Feed Trigger node. Add your sources:

• https://news.ycombinator.com/rss (Hacker News)
• https://techcrunch.com/category/artificial-intelligence/feed/ (TechCrunch AI)
• https://feeds.feedburner.com/TheHackersNews (Security news)

Set the polling interval to every 30 minutes.

Step 2: Keyword Filter

Add an IF node to filter articles. Check if the title or description contains your target keywords:

{{$json.title.toLowerCase().includes('ai') || 
  $json.title.toLowerCase().includes('llm') || 
  $json.description.toLowerCase().includes('edge computing')}}

This ensures you only process relevant articles, saving compute time.

Step 3: AI Summarization with Ollama

Add an HTTP Request node pointing to your local Ollama instance:

• URL: http://localhost:11434/api/generate
• Method: POST
• Body:

{
  "model": "llama3.1:8b",
  "prompt": "Summarize this article in 3 bullet points for a business audience:\n\nTitle: {{$json.title}}\n\nContent: {{$json.description}}",
  "stream": false
}

Since Ollama runs locally, this costs EUR 0 per request with ~12ms latency.

Step 4: Blog Post Generation

Add another Ollama call to generate a full blog post draft:

{
  "model": "llama3.1:8b",
  "prompt": "Write a 300-word blog post about this topic for a Spanish SME audience interested in AI deployment. Include practical takeaways.\n\nTopic: {{$json.title}}\nSummary: {{$node['Ollama Summary'].json.response}}",
  "stream": false
}

Step 5: Save for Review

Add a Google Sheets or Notion node to save the output:

• Title
• Original URL
• AI Summary (3 bullets)
• Draft blog post
• Suggested tags
• Status: “Draft”

Download This Workflow

We’ve built a production-ready version of this pipeline that includes error handling, deduplication, and multi-language support.

Download the n8n workflow JSON — import directly into your n8n instance.

Running Costs

Component	Monthly Cost
n8n (self-hosted)	EUR 0
Ollama + Llama 3.1 8B	EUR 0 (local)
Hardware (Mac Mini M4)	EUR 5/mo electricity
Total	EUR 5/mo

Compare this to using GPT-4o API for the same workflow: approximately EUR 50-200/month depending on volume.

Scaling and Quality Assurance

Once your pipeline is running, consider these enhancements to maintain content quality at scale:

• AI Evaluations: n8n’s built-in AI Evaluations feature lets you run test datasets through your workflow and measure output quality scores automatically — essential for catching quality drift before it reaches production.
• Multi-model routing: Use a Code node to route tasks to different models based on complexity. Simple summaries go to Phi-4 (fast), research-heavy content goes to Llama 3.3 70B (deep reasoning).
• Human-in-the-loop: Add an approval step via email or Slack before the publish node. Automation handles 90% of the work; a human validates the final 10%.
• Scheduled runs: Use n8n’s built-in Cron trigger instead of webhooks for daily or weekly content batches. This is more reliable than webhook-based triggers for content pipelines.

Related reading

• Build a Self-Improving AI Agent with n8n: The Learning Loop Workflow
• Guide Workflows Gstack Workflow
• n8n Workflow Automation: Complete Guide for AI-Powered Businesses

Related Resources

• 230 Downloadable Workflows — browse our full n8n + ComfyUI library
• ROI Calculator — compare cloud vs local AI costs
• Hardware Catalog — 13 devices for running n8n + Ollama locally
• Edge AI vs Cloud Cost Analysis — detailed cost breakdown

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