A practical architecture to cut unnecessary LLM calls: encoder, conformal prediction, deterministic routing
camerongreen95 · reddit · 2026-10-29
The author proposes an architecture to cut unnecessary generative inference in production: request → lightweight encoder → uncertainty check → deterministic action or LLM escalation. Most classification/routing decisions don't need an LLM, and smaller models can handle them far more efficiently.
Key challenges outlined:
- Establishing a reliable encoder baseline
- Calibrating uncertainty instead of trusting raw confidence scores
- Deciding when to escalate to an LLM
- Measuring end-to-end quality impact
- Monitoring behavior under production distribution shift
Conformal prediction is highlighted for its statistical coverage guarantees under suitable assumptions — though the author cautions coverage doesn't automatically translate into perfect routing under distribution shift. The post also promotes an Oct 30 Packt workshop with Ben Auffarth.
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