Key answer
Decomposition helps when it reduces error or rework costs, but adds communication, waiting and integration costs. Repeated background explanations, copied intermediate answers and “continue” replies without better quality suggest excessive fragmentation.
Full explanation
AI Chains' 20-person study found nine participants considered the interface more complex/harder to learn; four found relationships between local edits and final results difficult to understand. These interface-specific findings show that more visible steps do not necessarily mean easier use. Section 5.3.6
Ask whether a step has an independent deliverable, changes the next step, or exposes/localizes otherwise hard-to-find problems. Any clear role can justify retaining it. Turning read–think–organize–output into four waits usually cannot.
For 200 records, test field understanding on a small batch, then process the rest consistently. Do not require approval per record or independently invent inconsistent field definitions. When each record needs different evidence, internal individual checking can still matter without 200 user interactions.
Arrange reading, organization, verification and synthesis yourself and continue authorized work. Ask only about conflicting field meanings or changes to agreed goals. Combine similar questions and identify affected results; record progress for other steps without awaiting replies.
Compare total completion time, omissions, rework and user effort on similar tasks. If removing a checkpoint preserves quality/traceability and reduces waiting, do not restore it merely for a more complete-looking process.