Document Summarization inference

Design the workflow before choosing a model

Specify the audience, output structure, and facts that must be preserved. For long documents, evaluate whether chunking loses relationships across sections before adding a multi-stage pipeline.

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 factual consistency, required-fact coverage, length compliance, and human editing time.

Test tables, conflicting statements, long appendices, and source material with little relevant information. Check that the summary does not present uncertainty in the source as certainty.

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

Use a consistent rubric to compare candidate models. Measure total tokens across every summarization stage and include the cost of reviewing or correcting the final output.

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.