Give the model bounded work
The most dependable tasks are transformations with a clear shape: extract fields, compare documents, draft alternatives, classify a small set, or turn notes into a first outline. Open-ended prompts make it harder to tell whether the result is complete or merely plausible.
Keep the source material close
Good AI workflows make the relevant source easy to inspect. I prefer a small context window assembled from the actual project files over a broad prompt that asks the model to guess. The source, assumptions, and output should stay connected so a person can check the result quickly.
- Pass only the context needed for the current decision.
- Preserve links, filenames, dates, and quoted evidence where they matter.
- Separate generated suggestions from confirmed project facts.
Add review gates
A useful workflow says where a person must review, what they are checking, and what happens when the output is wrong. High-impact decisions, public claims, and irreversible actions need a clear human checkpoint. Automation should remove repetition, not remove responsibility.
Ship small workflows
I get better results by automating one repeated step and observing it in use before connecting five more. A small workflow is easier to debug, measure, and replace. Once it earns trust, it can become a reusable tool instead of a clever prompt someone has to remember.