Introduction to Reasoning › Teaching Reasoning Through Examples

Michael BrenndoerferJune 21, 20252 min read
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Just like with other prompting strategies, you can teach reasoning by showing examples. This is especially useful when you want the agent to follow a specific reasoning format or approach.

Few-Shot Reasoning

Here's how you might use examples to teach a reasoning pattern:

Question: If 5 apples cost \$3, how much do 8 apples cost?
Reasoning:
- First, find the cost per apple: \$3 $\div$ 5 apples = \$0.60 per apple
- Then multiply by 8 apples: \$0.60 $\times$ 8 = \$4.80
Answer: \$4.80

Question: If 3 books weigh 6 pounds, how much do 7 books weigh?
Reasoning:
- First, find the weight per book: 6 pounds $\div$ 3 books = 2 pounds per book
- Then multiply by 7 books: 2 pounds $\times$ 7 = 14 pounds
Answer: 14 pounds

Question: If 4 tickets cost \$50, how much do 9 tickets cost?

By showing the pattern in the first two examples, you're teaching the agent how to structure its reasoning for the third question. The model will recognize the pattern: find the unit rate, then scale up.

Example (GPT-5)

Here's how this looks in code:

In[2]:
Code
from openai import OpenAI

client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))

prompt = """Solve these word problems by showing your reasoning step by step.

Question: If 5 apples cost \$3, how much do 8 apples cost?
Reasoning:
- First, find the cost per apple: \$3 $\div$ 5 apples = \$0.60 per apple
- Then multiply by 8 apples: \$0.60 $\times$ 8 = \$4.80
Answer: \$4.80

Question: If 3 books weigh 6 pounds, how much do 7 books weigh?
Reasoning:
- First, find the weight per book: 6 pounds $\div$ 3 books = 2 pounds per book
- Then multiply by 7 books: 2 pounds $\times$ 7 = 14 pounds
Answer: 14 pounds

Question: If 4 tickets cost \$50, how much do 9 tickets cost?
Reasoning:"""

response = client.chat.completions.create(
    model="gpt-5", messages=[{"role": "user", "content": prompt}]
)

print(response.choices[0].message.content)

The agent will follow the pattern you've established, breaking down the problem into unit cost and then scaling up.