AI Prompt Engineering · Lesson

Zero-Shot and Few-Shot Prompting

Discover how to elicit responses with no examples (zero-shot) or by providing a few illustrative examples (few-shot).

Zero-Shot and Few-Shot Prompting is a free AI Prompt Engineering lesson on CoddyKit — lesson 2 of 3. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the AI Prompt Engineering learning path, one of 3 lessons in the course, and your progress syncs across the web and the CoddyKit app.

Intro to Zero & Few-Shot

Welcome to this lesson on Zero-Shot and Few-Shot Prompting! These are fundamental techniques for guiding Large Language Models (LLMs) to perform tasks.

Understanding them helps you get better, more precise outputs from AI.

Understanding Zero-Shot

Zero-shot prompting means giving an LLM a task without providing any examples of how to do it.

You rely solely on the LLM's pre-trained knowledge.
The prompt contains only instructions and the input data.
It's effective for simple, common tasks the LLM has likely seen during training.

Zero-Shot in Action: Classify

Here's a zero-shot example. We're asking the LLM to classify text sentiment without showing it any previous sentiment classifications.

Try running this example:

def call_llm_api(prompt):
    # In a real scenario, this would call an LLM API.
    # For this example, we simulate a response.
    if "sentiment" in prompt.lower():
        return "Sentiment: Positive"
    return "LLM Response: Could not process."

# Our zero-shot prompt
zero_shot_prompt = "Classify the sentiment of 'The movie was fantastic!'"

print(f"Prompt: {zero_shot_prompt}")
print(f"LLM Output: {call_llm_api(zero_shot_prompt)}")

Zero-Shot: Extract Information

Zero-shot prompting is also great for extracting specific pieces of information from text.

We just tell the LLM what to look for, and it uses its general knowledge to find it.

def call_llm_api(prompt):
    if "name and age" in prompt.lower():
        return "Name: Alice, Age: 30"
    return "LLM Response: Information not found."

# Another zero-shot prompt example
zero_shot_prompt = "Extract the name and age from 'My name is Alice and I am 30 years old.'"

print(f"Prompt: {zero_shot_prompt}")
print(f"LLM Output: {call_llm_api(zero_shot_prompt)}")

Limitations of Zero-Shot

While powerful, zero-shot prompting has limitations:

Ambiguity: Tasks that are open to interpretation might yield inconsistent results.
Novelty: If the task or concept is very new or niche, the LLM might struggle without examples.
Complexity: For multi-step reasoning or highly specific formatting, just instructions might not be enough.

Introducing Few-Shot Prompting

Few-shot prompting addresses zero-shot limitations by including a few input-output examples directly in the prompt.

These examples guide the LLM on the desired task, style, or format.
It's like showing a student a few solved problems before asking them to solve a new one.
This technique significantly improves performance on complex or ambiguous tasks.

Few-Shot in Action: Rewriting

Here, we provide an example of how to rephrase a sentence into active voice. This helps the LLM understand the specific transformation we want.

Observe how the example clarifies the task:

def call_llm_api(prompt):
    if "active voice" in prompt.lower() and "ball was thrown" in prompt.lower():
        return "Output: The boy threw the ball."
    return "LLM Response: Could not rephrase."

# Our few-shot prompt with one example
few_shot_prompt = """Rephrase into active voice:
Input: The mouse was chased by the cat.
Output: The cat chased the mouse.

Rephrase into active voice:
Input: The ball was thrown by the boy."""

print(f"Prompt: {few_shot_prompt}")
print(f"LLM Output: {call_llm_api(few_shot_prompt)}")

Few-Shot: Code Generation

Few-shot prompting is incredibly useful for generating code or adhering to specific output formats.

By showing an example of desired code (e.g., a function signature and body), the LLM can better replicate that structure.

def call_llm_api(prompt):
    if "function to add" in prompt.lower():
        return "def add(a, b):\n    return a + b"
    return "LLM Response: Code not generated."

# Few-shot prompt for code generation
few_shot_prompt = """Generate a Python function:
Example 1:
Input: Function to multiply two numbers.
Output:
def multiply(a, b):
    return a * b

Example 2:
Input: Function to add two numbers."""

print(f"Prompt: {few_shot_prompt}")
print(f"LLM Output:\n{call_llm_api(few_shot_prompt)}")

Choosing the Right Technique

How do you decide between zero-shot and few-shot?

Zero-Shot: Best for simple, well-understood tasks where the LLM's general knowledge is sufficient. It's concise and saves token space.
Few-Shot: Ideal for complex, nuanced, or custom tasks requiring specific formats or styles. It provides crucial guidance, improving accuracy but using more tokens.

Zero/Few-Shot Check

Which prompting technique is generally better for simple, well-defined tasks without needing specific examples?

Recap: Prompting Power

You've learned about two foundational prompting techniques:

Zero-shot prompting uses only instructions, leveraging the LLM's broad knowledge for simple tasks.
Few-shot prompting adds examples to the prompt, offering crucial guidance for more complex, nuanced, or custom outputs.

Mastering these allows you to interact with LLMs more effectively and achieve desired results!

Frequently asked questions

Is the “Zero-Shot and Few-Shot Prompting” lesson free?

Yes — the full text of “Zero-Shot and Few-Shot Prompting” is free to read here on the web, and the AI Prompt Engineering course includes 3 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the AI Prompt Engineering course, upgrade to CoddyKit PRO.

What will I learn in “Zero-Shot and Few-Shot Prompting”?

Discover how to elicit responses with no examples (zero-shot) or by providing a few illustrative examples (few-shot). You practise AI Prompt Engineering with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.

Do I need any experience to start AI Prompt Engineering?

No prior experience is required. AI Prompt Engineering on CoddyKit is structured for beginners through advanced learners; this is — lesson 2 of 3, so you can start here or from the beginning and move at your own pace.

How long does the “Zero-Shot and Few-Shot Prompting” lesson take?

Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.

Can I write and run code in this AI Prompt Engineering lesson?

Yes. Every AI Prompt Engineering lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.

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