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Multi-Source Content Winner Selection

Reusability

A system that fetches the same content from multiple sources (RSS, web scraping, AI generation) and algorithmically selects the highest quality version for storage

How It Works

Uses quality scoring across multiple data sources - RSS feeds, Firecrawl scraping, iFramely metadata extraction, and Gemini AI as fallback - then judges which source provided the best title, description, and content

Components

Define multiple data acquisition methods for same content

Establish quality scoring criteria for each content type

Implement judging algorithm to compare sources

Store winning version while logging source performance

Use AI models as intelligent fallback when other sources fail

When to Use

When building content aggregation systems where data quality varies significantly across sources and you need consistent, high-quality output

When Not to Use

For simple applications with single reliable data sources or when processing speed is more important than quality

Anti-Patterns to Avoid

Not logging source performance for future optimizationOver-engineering quality detection for non-critical contentFailing to handle cases where all sources return low quality

Example

“A news aggregator pulls an article from RSS (truncated), Firecrawl (403 error), iFramely (low quality), so it falls back to Gemini AI which searches and provides a comprehensive summary”