Building an AI Content Pipeline: From RSS Feed to Published Article in 15 Minutes

Automating content creation isn't about replacing human creativity—it's about handling the repetitive work so you can focus on strategy and refinement. Here's how to build a practical AI content pipeline that transforms raw information into publishable content.

The Complete Pipeline Architecture

A robust AI content pipeline consists of five core components:

Data ingestion - Collecting raw content from sources
Processing - Cleaning and structuring the data
AI generation - Creating the actual content
Quality control - Automated and manual review steps
Distribution - Publishing to your platforms

Real Example: Tech News Aggregation Pipeline

Let's walk through a concrete example that monitors tech RSS feeds and produces weekly summary articles.

Step 1: Data Collection

The pipeline monitors RSS feeds every hour using a simple Python script:

import feedparser
import sqlite3
from datetime import datetime, timedelta

feeds = [
    'https://techcrunch.com/feed/',
    'https://www.theverge.com/rss/index.xml',
    'https://arstechnica.com/feed/'
]

def collect_articles():
    articles = []
    for feed_url in feeds:
        feed = feedparser.parse(feed_url)
        for entry in feed.entries:
            if is_recent(entry.published_parsed):
                articles.append({
                    'title': entry.title,
                    'summary': entry.summary,
                    'url': entry.link,
                    'source': feed.feed.title
                })
    return articles

This runs on a cron job and stores articles in a database. Over one week, it typically collects 200-300 relevant articles.

Step 2: Content Processing

The system groups articles by topic using simple keyword clustering:

AI/ML: Articles containing "artificial intelligence", "machine learning", "ChatGPT"
Hardware: "processor", "chip", "GPU", "iPhone"
Software: "app", "update", "release", "beta"

Each cluster becomes a section in the final article.

Step 3: AI Content Generation

For each topic cluster, the system generates a 200-word summary using this prompt structure:

Summarize these tech articles into a cohesive paragraph:

[Article titles and summaries]

Requirements:
- 150-200 words
- Focus on the most significant developments
- Include specific company names and product details
- Write in a neutral, informative tone

The AI processes each cluster separately, ensuring focused, relevant summaries rather than generic overviews.

Step 4: Quality Control Automation

Before publication, automated checks verify:

Length requirements: Each section 150-250 words
Factual consistency: Cross-reference claims against source articles
Readability: Flesch reading score above 60
Duplicate detection: Compare against previous weeks' content

Articles failing these checks get flagged for manual review.

Step 5: Publication Workflow

The final article follows this template:

# Tech Week: [Date Range]

## AI & Machine Learning
[Generated summary]

## Hardware Updates
[Generated summary]

## Software Releases
[Generated summary]

---
*Sources: [Automatic source attribution]*

The system publishes to WordPress via API and schedules social media posts.

Performance Metrics

After three months of operation, this pipeline:

Processes 1,200+ articles weekly
Produces publication-ready content in 15 minutes
Achieves 85% pass rate on quality checks
Reduces manual content creation time by 70%

Implementation Timeline

Week 1-2: Foundation

Set up data collection scripts
Design database schema
Test RSS feed monitoring

Week 3-4: AI Integration

Implement content generation
Build prompt templates
Create quality control checks

Week 5-6: Publishing

Connect to CMS APIs
Set up automated scheduling
Build monitoring dashboards

Common Pitfalls to Avoid

Over-relying on AI: Always maintain human oversight for final approval. AI handles the heavy lifting, but editorial judgment remains crucial.

Ignoring source diversity: Monitor your RSS feeds regularly. Dead links or biased sources will degrade output quality.

Skipping quality controls: Automated checks catch 90% of issues, but that remaining 10% can damage your reputation.

Publishing without review: Even with quality controls, have a human review process for sensitive topics or breaking news.

Measuring Success

Track these metrics to optimize your pipeline:

Processing time: Target under 20 minutes end-to-end
Quality pass rate: Aim for 80%+ automated approval
Reader engagement: Monitor time on page and social shares
Editorial efficiency: Measure time saved vs. manual creation

Next Steps

Start simple with one content type and gradually expand. Focus on reliability over sophistication—a basic pipeline that runs consistently beats a complex system that breaks frequently.

The goal isn't to eliminate human creativity, but to automate the mechanical work that prevents you from focusing on strategy, analysis, and the creative elements that truly engage your audience.