AI Relevance Scoring

AI relevance scoring is the use of a language model (like Claude or GPT) to read each Reddit match and assign a numeric score indicating how well it fits the user's product or research target.

AI relevance scoring is the use of a language model (like Claude or GPT) to read each Reddit match and assign a numeric score indicating how well it fits the user's product or research target. It's the layer that filters keyword noise — a search for "reddit monitor" matches everything from monitoring tools to literal Reddit-using monitors — down to a usable set of matches.

A scoring system typically works like this: the user provides a product description and target buyer profile, the tool fetches raw keyword matches, and the language model reads each match in context and outputs a score from 1 to 10 along with a one-line reason. Matches below a threshold are filtered or de-prioritized; matches above are surfaced in the digest or dashboard.

RedNudge uses Claude for this layer and surfaces both the score and the reasoning so users can audit individual decisions. A match like "my Reddit notifier finally stopped pinging me at 3am" might score a 2 (about a different category of product entirely), while "I'm a B2B founder, looking for a tool that watches subreddits for keyword mentions and emails me a daily digest" scores a 9.

The quality of scoring depends heavily on the product description the user provides upfront and on the feedback loop. A vague description ("I sell marketing software") produces vague scoring; a specific description ("I sell a Reddit keyword monitoring SaaS for B2B founders priced at $19-99/mo, my buyers are solo founders and small marketing teams looking for lead-gen signal") produces sharply better scoring.

The feedback loop matters because language models drift on edge cases. Tools that learn from user behavior — dismissed matches downweighted, acted-on matches upweighted, dismissal reasons used to refine prompts — converge on each user's actual definition of relevance over a few weeks of use.

Related terms

Intent Tagging — Intent tagging is the practice of classifying each Reddit match by the type of intent expressed — buying, asking, complaining, comparing, or generic mention — so users can prioritize replies and research.
Dismiss-to-Train — Dismiss-to-train is the pattern where a user dismissing an irrelevant match in a monitoring tool teaches the underlying scoring system to filter out similar matches in the future.
Relevance Threshold — Relevance threshold is the minimum AI relevance score a Reddit match must reach to appear in the digest — the user-tunable cutoff between "send to me" and "filter out."
Signal vs. Noise — Signal vs. noise is the ratio of useful, actionable matches to total matches in any monitoring system — the central quality metric for Reddit keyword monitoring.