Learning Timeline
Key Insights
Gemini's Advantage for Transcripts
Gemini is incredibly powerful for video content enrichment due to its native YouTube integration, allowing you to retrieve full transcripts for Vector Embeddings.
Use Judge Logic
Don't rely on a single source. Build a 'Judge' function to select the best data (the Winner) between RSS, crawlers, and AI to ensure the highest possible content quality in your database.
Bypassing Reddit Restrictions
Most crawlers are blocked by platforms like Reddit, but Gemini's search feature often successfully retrieves the 'Ground Truth' of the content when other crawlers fail.
Cost & Speed Efficiency
Use GPT-4o mini (or other 'mini' models) for summarization (TL;DR) and final processing tasks, as it is significantly faster and more cost-effective than flagship models.
Prompts
Gemini Ground Truth Extraction
Target:
Gemini
Turn on your ground truth. Turn on your search. Go out and figure out what is this article actually about and provide the full content or transcript.
Step by Step
Article Enrichment Workflow Using Multi-Model AI
- Input the article URL or connect an RSS feed into the database system.
- Call the iFramely API to extract basic metadata such as 'Title', 'Description', and 'Rich Media' (images or embed codes).
- Use Firecrawl to perform a deep crawl on the URL to retrieve the full 'Body' content, bypassing the limitations of RSS feeds.
- Activate the Gemini API with the 'Ground Truth' or 'Search' feature to obtain additional context or a full transcript if the source is a YouTube video.
- Implement 'Judge' logic to compare the outputs from iFramely, Firecrawl, and Gemini.
- Select a 'Winner' (the best source) for each data category (e.g., the best Summary from iFramely, the best Main Content from Gemini).
- Send the 'Winner' data to GPT-4o mini for fast final processing, such as generating TL;DRs and Vector Embeddings.
- Save the enriched data into the database for display in the news application.