Turn a plain question into a reasoning prompt
Asking a model to “just answer” a multi-step problem often gets you a confident wrong answer. Chain-of-thought prompting fixes this by asking the model to reason before it concludes. This tool takes your plain question and wraps it in three proven CoT patterns — step-by-step, think-aloud and scratchpad — so you can copy whichever fits and let the model work the problem out loud.
How it works
Each style is a tested phrasing template. Step-by-step instructs the model to break the problem into explicit numbered steps and only then state the answer. Think-aloud invites freer narrated reasoning, which can surface considerations a rigid list misses. Scratchpad asks the model to do its working inside a clearly marked area and then give a clean final answer beneath it — useful when you want the reasoning available but the deliverable tidy. Your question is inserted into each template and the finished prompts are ready to copy.
Tips and notes
Reach for CoT on genuinely multi-step work — arithmetic, logic puzzles, planning, debugging, anything where intermediate steps matter. Skip it for simple factual lookups, where it only adds cost and latency. If you need a machine-parseable result, the scratchpad style is best because it separates the reasoning from a clearly delimited final answer you can extract. Note that some newer reasoning-optimized models already reason internally, so heavy explicit CoT instructions can be unnecessary or even counterproductive there — test both. Everything is generated locally, so pasting sensitive questions is safe.