A simple workflow for cleaner LLM work and lower wasted tokens
Complex tasks can make an LLM lose track of what matters or take shortcuts. This workflow splits a large job into smaller steps, creates s for those steps, and keeps a status-tracked list of items to process. The LLM then applies the right skill to each item and checks the list as it moves forward.
This makes it easier to see what is done, what is missing, and where quality may have slipped. The main goal is better on multi-step work such as analysis, , s, and other tasks that need careful follow-through.
Key points
- Break large tasks into smaller steps so the LLM does not handle too much at once.
- Turn repeated steps into s.
- Use to show what is finished, in progress, or still missing.
- The workflow aims to reduce lost context, skipped work, and sloppy output.
- It may reduce wasted tokens by cutting down on rework and repeated instructions.