AI classifier
Words made simple
A few useful meanings before you read the explanation.
- API
- A way for one piece of software to ask another for data or an action. A claims API might return the current status of a claim.
- LLM
- A model trained to work with language. It can write or summarize text, but its answer still needs checking against reliable information.
- Evaluation
- Checking results against examples and clear criteria to find whether the system works well and where it fails.
Overview
A classifier selects a category for input. An LLM classifier can handle varied wording with a bounded output label.
Why this matters
Requests need routing or prioritization before further processing.
An everyday example
Sort incoming mail into departments.
A technical example
Return claim_status, policy_question, or other.
How it works
- Define categories/examples
- Call rules/model
- Validate enum
- Route
- Measure errors.
Architecture and flow
Message → classifier → permitted route.
The insurance assistant example
Route complaints to a staff process rather than a FAQ answer.
When to use it
Language categories with sufficient evaluation examples.
When to avoid it
Exact routing by API endpoint or explicit user selection needs no model.
Interview answer
“Classification is a bounded decision; I test confusing boundaries and provide an unknown route.”
Benefits, trade-offs, and production considerations
Measure per-class precision/recall and asymmetric error costs. A classifier should not grant tool rights.
Can you explain it without the notes?
AI classifier
Use your own words. Explain an example and one limitation before checking the explanation.
Check your explanation
A classifier selects a category for input. An LLM classifier can handle varied wording with a bounded output label.
Turn this concept into a clear answer.
Practice this conceptAdapted from 13_AI.MD · “AI classifier”. Examples are study fragments; framework behavior can depend on your version.