Batch Classification inference

Design the workflow before choosing a model

Define a label taxonomy, validate input records, process bounded batches, and persist results with stable record identifiers. Make retry handling idempotent so repeated work does not duplicate downstream actions.

Define a representative input and an explicit acceptance criterion. Keep model and prompt versions with the result so quality changes can be investigated.

Evaluate outcomes and failure modes

Measure per-class precision and recall, failed-record rate, throughput, and cost per accepted label.

Use a held-out set that reflects class imbalance. Report rare-class errors separately; an aggregate accuracy score can hide failures on the categories that matter most.

Compare candidate routes on the same held-out examples. Report how many examples were evaluated and inspect failures rather than relying on a single average score.

Roll out with a measurable cost baseline

Pilot on a small batch with a manual review sample. Choose concurrency from measured provider limits and completion deadlines, then reconcile all records after retries.

Calculate cost per accepted outcome using input and output tokens, retrieval or tool fees, retries, and review effort. A lower token price is useful only if the total workflow still meets its requirements.

o10 can provide model routing for the inference steps. Your application remains responsible for workflow permissions, tool behavior, and deciding whether the final result is acceptable.