Small can describe parameter count, a narrow task or a local deployment. These differ. A smaller model is not automatically better, cheaper or independent of a supplier.
The decision to examine
Compare a simple rule, a specialised model and a general service on the same task. Count review, licensing, memory, updates and maintenance, and test language and local context.
Worked example
For hypothetical invitations in three categories, keywords may suffice. If wording becomes ambiguous, test a model and compare its errors with the rule. Novelty alone is not a reason to choose it.
From idea to practice
- Describe the problem as a need, not a product name.
- Specify evidence needed before a decision and who reviews it.
- Test a non-personal example and log failures as well as successes.
- State whether to continue, improve or stop and why.
Your exercise
Define the task, boundaries and five difficult cases. Choose the simplest method meeting your standard, including at least one case requiring human referral.
Review rubric
A useful answer identifies a context, checkable evidence, a boundary and an owner for the next step. If it fits any organisation unchanged, add a detail specific to the case. This is a learning rubric, not automatic marking or official assessment.
Further reading: AI Risk Management Framework — NIST · 2023; page reviewed 2026 · Checked: 8 October 2026
Your next step
Open the readiness notebook · Explore policy sources
Institutional DJAIRI material prepared with AI assistance. Learning examples are hypothetical; analysis and recommendations are Mustanef’s. Citing a source does not imply partnership or endorsement.