Cheap Model Bulk Task Boundary

Cheap models are useful for bulk tasks, but only when the task has a clear boundary and a verification method. Good examples include tagging support tickets, normalizing titles, drafting similar descriptions, extracting fields, grouping duplicate issues, or producing first-pass summaries. Bad examples include ambiguous architecture decisions, security review, novel debugging, or anything where a silent mistake is expensive.

The boundary starts with a schema. If the output can be validated as JSON, a fixed label set, a short summary, or a diff-free classification, a cheaper model can often do the first pass. If the output requires inventing a design, interpreting unclear intent, or changing production code, it should escalate.

Sampling is part of the workflow. Do not trust a thousand generated labels because the first five look good. Review a representative sample, check edge cases, and keep a fallback route for uncertain items. A cheap model should be allowed to return “unknown” rather than forcing an answer.

Cost control should not hide quality cost. If the cheap route creates cleanup work, review fatigue, or incorrect downstream automation, it is not cheap. Track accepted output rate, correction rate, and cases escalated to a stronger model.

The practical rule: use cheap models where the task is bounded, reversible, and measurable. Escalate when the task becomes ambiguous, high-risk, or hard to verify.

Cheap Model Bulk Task Boundary

// COMMENTS

ON THIS PAGE