7 Magic Words That Make Your LLM 10 Smarter at Math

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// cot.mjs

import { generateText } from "ai";

import { google } from "@ai-sdk/google";



const model = google("gemini-2.5-flash");

const problem = "Roger has 5 tennis balls. He buys 2 cans of 3 balls each. How many balls does he have now?";



const bad = await generateText({

  model,

  prompt: problem + "\n\nJust answer with the number, nothing else."

});



const good = await generateText({

  model,

  prompt: problem + "\n\nLet's think step by step."

});



console.log("=== Without CoT ===\n" + bad.text);

console.log("\n=== With CoT ===\n" + good.text);

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node --env-file=.env cot.mjs

Two runs of the same model on the same problem, side by side. The difference is visible immediately.

Levels of CoT

1. Zero-shot CoT (above)

Just add "Let's think step by step." Works on most modern models.

2. Few-shot CoT

Prepend 2-3 worked examples before the question:

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Q: Sara had 4 apples and got 2 more. How many?

A: Sara had 4. She got 2 more. 4 + 2 = 6. Answer: 6.



Q: Roger has 5 tennis balls. He buys 2 cans of 3 each. How many balls?

A: [model continues in same format]

Better on harder problems — the model has explicit examples of the reasoning depth you want.

3. Structured CoT

Force a format:

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"Solve this. Number your steps 1, 2, 3. Final answer on a new line starting 'Answer:'."

Easier to parse programmatically.

4. Hidden CoT

Generate the chain, then strip it before showing the user:

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const reply = result.text;

const clean = reply.replace(/<thinking>[\s\S]*?<\/thinking>/g, '').trim();

User sees just the answer; the model gets the accuracy benefit.

What about reasoning models?

GPT-5, Claude 4 Sonnet, o1, o3, Gemini 2.5 — modern flagship models train with reasoning baked in. They don't need "let's think step by step." They do it automatically.

But:

• They cost 10× more per token


• They're slower (visible "thinking..." UI)


• They're overkill for simple tasks

Cheap model + CoT prompt ≈ reasoning model output, at ~10% of the cost. CoT is still the highest-leverage technique you can use on small models.

What this unlocks

CoT is the foundation. Every fancier reasoning technique builds on top:

• 
  Self-consistency — sample N CoT runs, take majority vote


• 
  ReAct — CoT + tool calls interleaved (Day 1)


• 
  Tree of Thoughts — branch CoT into multiple paths, evaluate


• 
  Reflection — generate, criticize own output, regenerate

Master CoT first. Everything else is variations.

Try it now

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