Over the past months I’ve tested a set of reasoning workflows that significantly improve output quality for both small and large models. They’re not about “prompt tricks” — they’re about structuring a task so the model can reason cleanly.

Here are the 6 core skills that consistently make the biggest difference:

1. *Decomposition* – splitting a vague task into constraints, context, steps, assumptions. 2. *Constraint stacking* – defining what must be true and what must not happen. 3. *Reasoning path control* – explicit “think aloud but check your assumptions” steps. 4. *Refinement loops* – generate → critique → adjust → regenerate. 5. *Verification passes* – hallucination checks using independent re-reasoning. 6. *Output benchmarking* – defining evaluation criteria before generating anything.

I wrote up an overview of these workflows, with examples and a full breakdown here: https://dailyaiguide.substack.com/p/ai-reasoning-workflows-t...

If anyone is interested, I can also share: - detailed decomposition frameworks, - verification chains, - or optimized workflows for specific tasks (analysis, learning, planning, writing).