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A model change can alter behaviour even when your application code stays the same. Prompts, retrieval indexes, tool schemas and reference data also form part of the deployed system.
Define the release unit
Record the application version, model identifier, prompt version, retrieval configuration and tool schema versions. Keep a clear link between an evaluation run and the configuration it actually tested.
For a document assistant, include the source snapshot or index version. Otherwise, a result may be impossible to reproduce after the underlying documents change.
A practical release review
- Does the new configuration improve the tasks that motivated it?
- Which previously passing cases now fail?
- Have permission and unsupported-question cases been checked?
- What has changed in latency and cost?
- Can the previous configuration be restored?
- Who will review user reports and failed requests?
Start with a small controlled rollout when the environment supports it. Define rollback conditions in advance. “The new version feels better” is difficult to act on during an incident.
Monitor behaviour, not just uptime
A healthy server can return unhelpful answers all day. Watch validation failures, source support, escalation patterns and task outcomes alongside infrastructure metrics. Preserve privacy when selecting examples for review.
Discussion: Which component of an AI application do teams most often forget to version?
Reference: Hugging Face model-card documentation.
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