Final artifact
A model-routing matrix, an unsupported-answer policy, and a fallback ladder.
Assessment
This audit forces you to write explicit policy for task classes, model-path choices, unsupported-answer behavior, and fallback order. DepthPilot cares less about the claim that one model is stronger and more about whether you can explain which requests should use which path, when the system should abstain, and how those decisions can be reviewed.
A model-routing matrix, an unsupported-answer policy, and a fallback ladder.
Not that the system usually answers, but that it takes the right path when it should clarify, retrieve, abstain, or escalate.
This page turns routing, abstention, and downgrade behavior into executable policy instead of vague anti-hallucination advice.
Classify tasks by value, risk, and evidence need instead of by provider preference.
Give each class a latency and cost budget so not every request goes through the strongest path.
Define which tasks should never auto-answer.
Make routing rules reviewable by a second operator.
Treat clarify, retrieve, abstain, and escalate as legitimate outcomes instead of failures.
Define the response to missing evidence, stale evidence, missing authority, and high-risk action requests.
Write the user-facing language so unsupported answers are explicit instead of hidden behind vague prose.
Treat refusal as a quality mechanism, not as product embarrassment.
Define whether the primary route should fall back to retrieval, clarification, downgrade, or abstention.
Separate downgrade from abstain: downgrade still delivers a bounded result, abstain means the system should stop answering.
Make the hard stop and human owner explicit.
Turn fallback into an explicit ladder instead of an improvisation.
Proof you must keep before launch
One routing matrix that makes task class, risk, evidence need, and budget explicit.
One unsupported-answer policy that covers clarify, retrieve, abstain, and escalate paths.
One fallback ladder that shows how the workflow degrades in order instead of jumping randomly.
One short recap explaining whether the workflow is currently most threatened by over-answering, overspending, or chaotic fallback behavior.
Reusable routing templates
Bind task class, risk, evidence need, and budget to a concrete model or path.
Write explicit policy for clarify, retrieve, abstain, and escalate behaviors.
Make downgrade, abstain, and escalation order explicit.
Search Cluster
High-intent users often enter through model-routing, limitations, or eval-checklist searches before they commit to real unsupported-answer policy design.
LLM Model Routing Guide
Many users search for model routing by asking which model is strongest. DepthPilot focuses on a harder question: which requests deserve the strong path, which should take the cheaper path, and which should not answer directly at all.
Open pathLLM Limitations
Users searching for LLM limitations often only want a list of weaknesses. DepthPilot pushes further: you should learn how to route tasks into direct answer, clarification, retrieval, tool use, or refusal so fluent output stops stealing your judgment.
Open pathAI Eval Checklist
Users searching for an AI eval checklist usually do not lack opinions. They lack an executable review frame. This page condenses the minimum eval logic into a checklist-style entry point.
Open pathReference appendix
These links anchor the method. The actual lesson is the routing matrix, unsupported-answer policy, fallback ladder, and deliverable templates above.