Zero-Shot vs. Few-Shot Prompting in AI - What’s the Difference & Why It Matters?
- Tanu Varshney

- Sep 17
- 2 min read

One of the biggest questions I get asked about AI is:
“How do prompts actually influence the output?”
The answer comes down to how much context you give the model
and that’s where Zero-Shot, Few-Shots come in.
🔹 Zero-Shot Prompting
Zero-Shot means: no examples, just instructions.
The model relies purely on its pre-trained knowledge to answer.
Example : Writing Code
Prompt:“Write a Python function that fetches weather data for a given city.”
Best for:
Common tasks (summarization, standard coding, quick explanations).
Speed over precision.
⚠️ Pitfall: Outputs can be inconsistent in structure or style.
🔹 Few-Shot Prompting
Few-Shot means: show the model a few examples first so it learns the style, structure, or logic you want.
Example : Consistent Code Style
# Example 1: Fetch stock price
def get_stock_price(symbol):
response = requests.get(f"https://api.stock.com/{symbol}")
return response.json()["price"]
# Example 2: Fetch user profile
def get_user_profile(user_id):
response = requests.get(f"https://api.users.com/{user_id}")
return response.json()["profile"]
# Task: Fetch weather data for a given city
def get_weather(city):
???
Best for:
Structured or repeatable outputs (SQL, JSON, APIs).
Consistency in style and format.
Guiding the model when tasks are tricky.
Chain-of-Thought (CoT) Prompting
Chain-of-Thought means: asking the model to “think step by step” before giving the final answer.
This is especially powerful for reasoning tasks like math, logic puzzles, or multi-step queries.
Example : Math Problem
Prompt:“If there are 12 apples and 3 people share them equally, how many apples does each person get? Explain step by step.”
Output:
There are 12 apples.
Divide 12 by 3.
12 ÷ 3 = 4.
Each person gets 4 apples.
Best for:
Complex reasoning (math, logic, planning).
Preventing “hallucinated shortcuts.”
🔹 Few-Shot Chain-of-Thought (Few-Shot-CoT)
This is the most powerful combo:
You give examples, and
You ask the model to show its reasoning.
Example : Few-Shot-CoT for Word Problems
Example 1:
Question: A car travels 60 km in 2 hours. What is its speed?
Answer:
Step 1: Speed = Distance ÷ Time.
Step 2: 60 ÷ 2 = 30.
Answer: 30 km/h.
Example 2:
Question: A train covers 120 km in 4 hours. What is its speed?
Answer:
Step 1: Speed = Distance ÷ Time.
Step 2: 120 ÷ 4 = 30.
Answer: 30 km/h.
Question: A bike covers 90 km in 3 hours. What is its speed?
Answer:
Now the model will follow your reasoning style exactly.
Best for:
Teaching the model how to reason in your way.
High-stakes tasks (finance, science, structured decision-making).
The Takeaway
[ Zero-Shot ] → Quick answers, no examples
↓
[ Few-Shot ] → Guided by examples, consistent style
↓
[ Chain-of-Thought ] → Step-by-step reasoning
↓
[ Few-Shot-CoT ] → Examples + reasoning → most reliable
Think of it like :
Few-Shot-CoT (Most Reliable )
Chain-of-Thought (Step-by-step logic )
Few-Shot (Consistency )
Zero-Shot (Fast )
That’s the progression from basic prompting to advanced AI collaboration.



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