Platform ✦ 90outlineCalibrate thresholds on your traffic

Route to cheap vs expensive LLM

Who: Platform / AI eng owning multi-model stacks. Steps: (1) State = user request (+ risk tags). (2) Choice difficulty/risk or Score complexity. (3) Code picks small LLM vs frontier vs abstain. Expected effect: spend frontier tokens only when needed.

1

Treat this as an outline — adapt state and questions to your data.

2

Implement in code — call System One / Jev; compose answers yourself.

3

Gate on confidence — act, confirm, or escalate before side effects.

Outline sketch

Prompt
Audience: platform / AI eng.
Steps:
1) State: user request, tools, budget, risk tags.
2) Choice difficulty/risk or Score complexity; Noul needs_strong_model.
3) Code routes cheap vs frontier vs abstain/human.
Expected effect: spend frontier tokens only when justified.

Needs access to: typesafe-sdkllm-gateway

Who it's for

Platform / AI eng owning multi-model stacks

Steps / how it's set up

Sources: LangChain · Building a harness with Jev · confidence-routing · awesome-jev-usecases · Harness.

Treat confidence as a second axis beside category: cheap default model when confidence is high; escalate to frontier / human when low. Community harness numbers are author-reported.

Sources (wave-2 deepen)

Expected effect

Harness pattern: System One decides when System Two is worth paying for.

Unofficial outline for learning. Paraphrased from public docs and tutorials — not a production recipe. Review sources before you automate anything.