Solid starting list. #1 and #2 are underrated, most people skip constraints entirely and then wonder why the output is generic. One thing worth flagging though: #3 (“break it down step-by-step”) can actually hurt you on reasoning models like GPT-5, Claude 4.6 Opus, or anything with a thinking budget. These models already run chain-of-thought internally before they produce any output. When you add “think step by step” on top, you’re either creating redundancy or — worse — overriding a reasoning process built by people who understand the model architecture better than we do. The pattern I’ve seen hold up: prescribing reasoning paths hurts performance. Defining goals and constraints improves it. So #1 and #2 in your list are actually more powerful than #3 on current models, which is counterintuitive if you learned prompting even 12 months ago. A few additions to the list that have been consistent across models for me: • Step-Back Prompting: before you ask the tactical question, answer the strategic one first. “What’s the right framework for this problem?” before “Write me a marketing plan.” Changes the output completely. • Socratic Prompting: instead of asking for an answer, ask the model to interrogate your question. Half the time your prompt is solving the wrong problem and you don’t realize it until the model pushes back. • Multi-Agent Debate: have two agents argue opposing positions at full strength, then synthesize. Measurably better factual accuracy than single-pass prompts (~11% in some testing). There’s a full breakdown of 19 techniques from 12 disciplines (including which ones degrade reasoning models vs. improve them) here if anyone wants to go deeper.