Models and model selection
Choose a model by task quality, latency, cost, privacy, and reliability—not reputation alone.
- 11 minutes
- intermediate
- Reviewed 2026-07-16
What is it?
A model transforms input context into predicted output. Models differ in reasoning, language, tool use, modality, speed, price, hosting, and consistency.
Why does it matter?
Architecture inherits model limits. A capable but slow model can ruin an interactive flow; a cheap model can be expensive if failures create rework.
The mental model
A model is an engine with trade-offs. Select the smallest reliable engine for the task and test it with real examples.
A simple example
Use a fast model for routine classification, then route uncertain cases to a stronger model or human reviewer.
What it is not
A benchmark winner is not automatically best for your data, language, latency target, or risk level.
Learn this first
These ideas make the lesson easier to place.
- AI models
- Deterministic versus AI decisions
Your first 60 minutes
Use one focused hour to make the idea concrete.
- Collect ten representative inputs and expected outcomes.
- Test two model options with the same prompt and schema.
- Compare quality, latency, failures, and estimated cost.
Build this first
Create a small evaluation sheet that routes simple classification to one model and escalates low-confidence cases.
When not to use it
Do not use a model where exact rules, database queries, or ordinary search answer the problem reliably.
What to learn next
Learn structured output and evaluation.