Optimize
AI Cache Optimization Assistant
Use CacheDB’s optional AI Assistant to diagnose cache behavior and validate policy candidates under operator control.
The optional CacheDB AI Cache Optimization Assistant is integrated into the console for teams that want help interpreting real proxy behavior. It is not an autonomous production optimisation system: its role is to investigate, recommend, and validate a candidate while the operator retains control of activation.
Work from operational context
The assistant can examine cache metrics, query patterns, invalidation relationships, cached instances, runtime signals, and script state. It can help answer practical questions such as:
- Which query shapes have low cache value or excessive invalidations?
- Is a policy invalidating too broadly for the observed traffic?
- Which TTL or cache-eligibility change is worth testing?
- What does a query fingerprint or cache relationship indicate about an application path?
This is more useful than a generic code suggestion because the analysis is tied to context already available at the proxy and console.
Produce a reviewable candidate
The assistant can propose focused Rhai changes for cache eligibility, TTLs, or selective invalidation. Candidates can be validated with the production policy engine without deployment. The operator reviews the resulting code, tests it against representative workload conditions, and uses the normal policy publication and audit process to activate it.
The assistant does not turn a suggestion into a production cache policy on its own.
Keep the boundaries explicit
AI support is optional and configured for the deployment. The provider, network path, credentials, retention expectations, and privacy requirements remain operator decisions. CacheDB uses bounded, domain-specific operations for diagnosis and policy work rather than exposing arbitrary infrastructure control to the assistant.
As with any cache-policy change, review the possible consistency impact, validate against the intended workload, and retain normal change-control evidence. Do not use an AI recommendation as a substitute for understanding application data semantics.
A useful evaluation starting point
Begin with transparent traffic observation and an existing policy or a concrete cache concern. The evaluation can then compare the assistant’s diagnosis with query context and a reviewed candidate, without making an uncontrolled change to production behavior.
Discuss an AI Assistant evaluation for a workload or policy you want to examine.