When is standard natural language processing a better choice than a generative model?
Why B is the answer
When a simple, repeatable classification meets the need
Standard NLP can be cheaper and more predictable than a generative model when a simple, repeatable classification is enough.
In detail
- A simple, repeatable classification suits standard language processing.
- Choose generative models for open-ended work.
Use case
An illustrative scenario, not a real incident.
A team routes tickets into four fixed categories. Standard NLP does this cheaply and gives steady results.
See it in different ways
stateDiagram-v2 [*] --> Requirement Requirement --> Copilot: Copilot already meets it Requirement --> Extend: Copilot needs your data or tools Requirement --> Build: needs its own logic Build --> CustomModel: existing models fail a measured need
flowchart TD
Q{"What is needed?"}
Q -->|"Fixed classification"| A0["Standard NLP"]
Q -->|"Open-ended text"| A1["Generative model"]Key point: Use the simplest method that works.
Why the other options are wrong
- A. When a user wants open-ended conversation
- Open-ended conversation suits a generative model.
- C. When you need a long, creative written response
- Creative text suits a generative model.
- D. When the answers must be drawn from many documents
- Synthesis across documents suits a generative model.