Intent Classification

2 posts

Corpus Drives Codebook — Why Your Intent Taxonomy Is Stuck at 60% and How It Evolves from 36 to 48 | Agentic AI in Practice (X)

Customer-service Agent in production, 36 intents, unknown rate 40%, the business side asks 'can we just add an LLM fallback?' The real problem is not the classifier — it's the codebook itself. Five minutes in you can spot the wrong diagnosis ('unknown rate high = classifier weak'); ten minutes in you have the four-quadrant test that filters 80% of pseudo-missing-intent requests; twenty minutes in you have the corpus → codebook iteration loop that evolves a taxonomy from 36 to 48 stable intents.

May 28, 2026·14 min read

Intent Classification for Chatbots: Why Pure-Rule and Pure-LLM Both Fail (a 3-Tier Cascade)

Intent classification is the first node in any customer-service Agent — get it wrong and the next four architecture decisions are wasted. Pure-rule is brittle; pure-LLM blows the budget. The 3-tier fallback (rule → embedding → LLM) is the only engineering trade-off that stands up. Five minutes in you can spot the two fake architectures ('just use an LLM' / '100% rules'); ten minutes in you have starting thresholds for all three tiers; twenty minutes in you have the signals that say it's time to evolve from HybridClassifier to LLM Router.

May 25, 2026·16 min read