LLM routing: choosing the right approved model for each task

Different tasks have different requirements

A short summary and a conflicting-document analysis need different capabilities. AWS documents intelligent prompt routing as one approach to balancing quality and cost within its supported service. Enterprise routing must first restrict destinations according to data policy; speed and price cannot authorize disclosure.

For more on this topic, read When is a small model enough for an organization? in the Liyan knowledge base.

An editorial starting configuration

Define low-risk short tasks, grounded document answers and complex analysis as separate trial groups. Specify approved models, time budgets and acceptance criteria for each. Begin with explainable rules. Diagnose retrieval failures before escalating to a larger model: missing evidence is not reliably repaired by more model capacity.

Fallbacks inherit the same constraints

A fallback must preserve residency, tool permissions and output contracts. Record model version and routing reason because behavior can change across models. Bound retries and escalation costs. For a sensitive workflow, an explicit stop may be preferable to silently choosing an ineligible destination.

Measure each branch and the completed task

Evaluate acceptance rate, latency, cost and human review for each branch. Compare cost per successful task, including retries and manual correction, rather than token price alone. Repeat evaluations after model-version changes. Report savings from the actual workload without assuming a universal improvement percentage.

Define task classes after policy eligibility

Compare field extraction, grounded policy answers and conflicting-document analysis with explicit output, data and latency requirements. A short question need not be low-risk or easy. Exclude processing environments that violate data rules before optimizing quality or cost. Test those exclusions with synthetic sensitivity labels and record the routing rationale without leaking source content into general logs.

Escalate for a diagnosed reason

A validation failure may warrant repair; missing evidence requires retrieval investigation rather than merely a larger model. Separate temporary service errors, unreadable inputs and permission failures. Give each path a bounded retry policy. Carry the reason for escalation forward, and avoid expensive chains that cannot establish when a task is complete.

Evaluate successful-task cost and release gradually

Record the selected path, attempts, end-to-end time, processing cost and independent acceptance outcome. Divide total cost by genuinely accepted tasks, then break the result down by task class and include review effort. First compare proposed routes in shadow evaluation, then release narrowly with versioned rules and rollback conditions. Outage fallbacks must preserve data, tool and output constraints. When no compliant fallback exists, return a clear controlled failure rather than silently relaxing policy.

A release exercise for routing policy

Write the expected route for a controlled set of extraction, grounded-answer and conflict-analysis requests. Include short but consequential questions, unreadable attachments, absent sources and policy-restricted data. Record which models are eligible before ranking them by cost or quality. A cheap ineligible destination must never become an outage fallback simply because the preferred environment is unavailable.

Compare proposed decisions with expert labels in a shadow run that does not change user-facing answers. Inspect incorrect task classifications and reasons for escalation. A missing source should not automatically trigger a stronger model, and an unreadable scan should not consume an unbounded sequence of retries. Give each failure category an explicit action, budget and stopping condition. Preserve enough technical rationale to investigate routes while avoiding sensitive content in broadly accessible logs.

When releasing to a limited group, keep the policy version and rollback conditions alongside model and tool configuration. For each completed task, record all attempts and independent acceptance, including reviewer corrections. Report cost and time by task class because a workload mix change can alter averages without improving any route. Rehearse model outage and budget exhaustion. An acceptable alternative may be a queued task or a clear request for review, depending on the workflow. The release decision should rest on accepted-task outcomes under normal and failure conditions, rather than the percentage of requests sent to a cheaper model.