You asked the AI a complex question. It gave you an answer instantly. The answer was wrong.
You asked again, this time saying 'explain your reasoning.' Suddenly the answer was right.
Same AI. Same question. Different result. The only difference: you made it think out loud.
The problem isn't the AI's knowledge. It's that you're letting it jump to conclusions instead of walking through the logic step by step.
PROMPT ENGINEERING PATTERN - A technique that dramatically improves reasoning quality by making the AI show its work before giving an answer.
Chain-of-thought prompting is exactly what it sounds like: you ask the AI to walk through its reasoning step by step before arriving at an answer. Instead of jumping straight to a conclusion, it breaks the problem into pieces, works through each piece, and only then synthesizes a final response.
This matters because language models are fundamentally pattern matchers. When you ask a complex question, the model's first instinct is to pattern-match to similar questions it's seen. That works for simple queries. For anything requiring actual reasoning, it fails spectacularly. By forcing intermediate steps, you give the model's 'attention' something to attend to. Each step becomes context for the next.
Without chain-of-thought, the AI guesses. With it, the AI reasons. The difference in accuracy on complex tasks can be 40% or more.
Chain-of-thought solves a universal problem: how do you get better decisions from any reasoning process by making intermediate steps explicit instead of hidden?
Break complex problems into explicit steps. Make each step visible and reviewable. Use the output of each step as input to the next. Only synthesize a final answer after all steps are complete. This pattern applies whether you're asking an AI to reason, debugging code, or making business decisions.
Same question. Same context. Completely different answers depending on whether the AI shows its work.
Should we approve this $4,500 software purchase?
Yes, approved. The tool will improve productivity.
No reasoning visible. No way to verify the logic. No audit trail.
Add "think step by step" to your prompt
The simplest approach. You add a phrase like "Think through this step by step" or "Explain your reasoning before answering." The AI generates its own intermediate steps without examples. Works surprisingly well for many tasks.
Show examples of good reasoning
You provide 1-3 examples of problems being solved with explicit reasoning steps. The AI learns the pattern and applies it to new problems. The examples teach both the structure of reasoning and the depth expected. Much more reliable than zero-shot.
Force specific reasoning stages
You define explicit stages the AI must complete: 'First, identify the constraints. Second, list possible approaches. Third, evaluate each approach. Fourth, select and justify.' The AI fills in each stage. Maximum control over reasoning.
A purchase request comes in with vague justification. The AI doesn't just say 'Approved' or 'Denied.' It walks through your expense policy, evaluates ROI, checks budget availability, and explains its recommendation step by step. Finance reviews the reasoning, not just the answer.
Hover over any component to see what it does and why it's neededTap any component to see what it does and why it's needed
Animated lines show direct connections · Hover for detailsTap for details · Click to learn more
You ask 'What year was the company founded?' and force chain-of-thought reasoning. The AI writes three paragraphs about how to determine founding dates, historical records, and verification methods before finally saying '2015.' You wasted tokens and time on a question that needed a one-word answer.
Instead: Reserve chain-of-thought for tasks that actually require reasoning. Simple factual lookups, classifications, and translations usually work better without it.
Your chain-of-thought prompt works great in testing. In production, the AI occasionally goes off on tangents. One response reasons for 800 tokens before getting to the point. Another hallucinates facts in its reasoning that contaminate the final answer. Users are confused.
Instead: Set token limits on reasoning sections. Validate intermediate steps programmatically when possible. Log and review reasoning chains regularly. Use structured CoT with explicit checkpoints.
You force the AI to reason in exactly 7 steps because more steps feels more thorough. For simple problems, the AI pads its reasoning with nonsense to hit the step count. The extra steps actually introduce errors and confusion.
Instead: Let the problem dictate step count, not a rigid template. Say 'think through the necessary steps' rather than 'explain in exactly 5 steps.' For structured CoT, make stages logical, not arbitrary.
You've learned how to structure prompts that encourage step-by-step reasoning. The natural next step is learning how to verify that reasoning is consistent and reliable across multiple attempts.