AI Relevance Scoring
A machine learning approach to evaluating scraped web content to determine how closely it matches a user's specific informational intent.
Definition
Keyword alerts often trigger false positive notifications. AI relevance scoring resolves this by processing newly discovered text through Large Language Models (LLMs) instructed with the user's exact goal.
The output is typically a quantitative score (e.g., 0 to 100), allowing systems to only notify users when the threshold of relevance is met.
Key Concepts
Semantic Understanding
Going beyond keyword matching to comprehend the actual meaning and context of the text.
Noise Reduction
Automatically discarding boilerplate text, cookie banners, or unrelated sidebars.
Fallback Chains
Routing requests to different AI models (e.g., Llama, Gemma, Qwen) depending on availability to ensure continuous scoring.
Why it Matters for Watchflare
Every piece of content scraped by Watchflare is run through a multi-model AI fallback chain to assign it a strict 0-100 relevance score, filtering out all noise natively.
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