Key Intelligence Takeaways
- Define an evaluation set before comparing models.
- Different workflow steps may need different model capabilities.
- A smaller reliable model can be better than a larger unnecessary one.
Model names change quickly, but the business task remains the useful anchor. A model should be evaluated against the documents, language, structure, tools, and quality threshold that the workflow requires.
The selection process should also include the operational conditions around the model. Response time, cost predictability, access controls, and provider terms may matter as much as output quality.
ANALYSIS // 01
Define the evaluation set
Collect representative examples, including ordinary, incomplete, ambiguous, and difficult cases. Define what a good response must contain and what would make it unacceptable.
The evaluation set should be protected as an organisational asset and updated when the workflow changes.
- ✦Typical examples
- ✦Edge cases
- ✦Expected properties
- ✦Failure conditions
ANALYSIS // 02
Score what the workflow needs
Compare factuality, instruction following, format compliance, consistency, latency, cost, and review effort. A model that produces fluent but hard-to-check answers may create more work than it removes.
Use human review where quality cannot be judged automatically, and record disagreements rather than hiding them in one score.
- ✦Quality
- ✦Latency
- ✦Cost
- ✦Review effort
ANALYSIS // 03
Plan for change
Keep the prompt, evaluation set, output contract, and model configuration documented. When a provider or model changes, rerun the evaluation before treating the change as invisible.
This gives the team a reliable way to improve without relying on assumptions about model versions.
- ✦Version record
- ✦Regression test
- ✦Change approval
- ✦Fallback option
SYSTEM SPECIFICATION
Turn this insight into a working production system
This insight connects directly to AI Infrastructure. If this operational friction affects your organization, schedule an engineering discovery audit to scope the automated workflow.
AUTHOR & INTELLIGENCE LEAD
Elisha Roodt
System Engineering Team
Published natively by the technical execution arm of Artificial Intelligence South Africa. Focused on deployment reality, avoiding speculation, and advancing the South African technology tier.