Chain of thought

Chain of thought is a technique that allows AI to answer by writing down the solution process step by step rather than just making a conclusion. Significantly improves accuracy for complex problems.

Thinking chain is a technique that allows AI to answer by writing down the solution process step by step, rather than just coming up with a conclusion. It is the same principle that in math tests, students who write solutions step by step make fewer mistakes than students who only write answers using mental calculations.

LLM appeared to improve the problems that frequently get wrong in complex reasoning problems, and it became famous when it was known that adding instructions to think step by step greatly improved the accuracy of math or logic problems. Recent inference-specialized models have reflected this method in their internal learning and developed a form of thinking for a long time before answering.

However, there are studies that show that the written solution process does not always match the actual calculation process inside the model. You should not blindly believe that there is a process, as you may come up with a wrong answer along with a plausible solution.

✅ Why it matters

• Significantly improves accuracy in complex math, logic, and planning problems
• You can see the solution process so you can check where you went wrong
• You can easily use it just by adding a line to the prompt.

⚠️ Limits and debates

• Longer output increases response time and cost
• There is research showing that written solutions may differ from actual internal judgment processes
• Wrong answers accompanied by a plausible process can actually look more persuasive