Design LLM-powered ranking for smart context selection
Follow up to:
#3576100
**Description:**
AI Context selection is currently deterministic. Future smart selection may use an LLM to re-rank or filter candidate context items, but this should be designed separately from the RC1 deterministic pipeline.
Key decisions to resolve:
- Whether LLM ranking should run synchronously during selection or asynchronously ahead of time.
- What candidate limit should be sent to the model.
- How ranking calls should be cached.
- What cache TTL and invalidation rules should apply.
- How token and cost budgets should be enforced.
- How failures should degrade back to deterministic ranking.
- Whether ranking should be implemented as an event subscriber or a dedicated smart-selection plugin.
This is suitable for post-RC1 because the deterministic selector can remain stable, and LLM ranking can plug into the selection pipeline later as an optional enhancement.
issue