other

LLM Classification Is Feature Engineering

A blog post arguing that a large language model's classification output should be consumed as an input feature to a fitted statistical model instead of being used as the final label.

Current clusters

build1 publisher

A two-parameter fit on your own merges puts a threshold under an LLM reviewer's "block"

A dev.to post argues that the approve and block labels AI review tools print are uncalibrated text, and proposes fitting sigmoid(alpha + beta * verdict) to a team's own review history so triage gets a threshold it can defend.

Publishers:dev.to

Reality

Evidence30
Adoption: Insufficient
Hype gap+15
Incentives35
Confidence45