Zero-Shot vs Few-Shot Prompting: When to Use Each (2026)

I spent months using ChatGPT before I understood why my prompts were so inconsistent.

Sometimes I’d get exactly what I wanted. Other times, the same type of request would give me completely different (and often worse) results. It felt random until I learned about zero-shot and few-shot prompting—two fundamental approaches that determine how AI interprets your requests.

The distinction sounds technical, but it’s actually intuitive once you see it in action. And understanding when to use each approach is probably the single biggest improvement you can make to your prompt engineering skills.

Let me show you exactly what these terms mean and how to use them effectively.

What Is Zero-Shot Prompting?

Zero-shot prompting is when you ask an AI to perform a task without providing any examples. You simply describe what you want, and the model figures out how to do it based on its training.

Example:

Classify this customer review as positive, negative, or neutral:

"The product arrived on time, but the packaging was damaged. The item itself works great though."

You’re asking the AI to classify sentiment without showing it any examples of how you want classifications done. The AI relies entirely on its pre-trained understanding.

When Zero-Shot Works Well

• Common, well-defined tasks - Translation, summarization, basic classification
• Clear instructions - When the task is unambiguous
• General knowledge questions - Things the model understands from training
• Quick, one-off requests - When you don’t need consistency across many items

When Zero-Shot Falls Short

• Domain-specific tasks - The model may not understand your specific context
• Unusual formatting needs - Hard to communicate exact output format without examples
• Consistent style requirements - Difficult to maintain consistency without references
• Novel task types - Tasks the model hasn’t seen in training

What Is Few-Shot Prompting?

Few-shot prompting includes examples in your prompt before asking the AI to perform the task. You’re essentially teaching the model what you want by showing rather than telling.

Example:

Classify these customer reviews:

Review: "Absolutely love it! Best purchase I've made all year."
Classification: Positive

Review: "It broke after two weeks. Complete waste of money."
Classification: Negative

Review: "It's okay. Does what it's supposed to, nothing special."
Classification: Neutral

Now classify this review:
Review: "The product arrived on time, but the packaging was damaged. The item itself works great though."
Classification:

By showing examples first, you’ve demonstrated exactly what “positive,” “negative,” and “neutral” mean in your context. The AI now has concrete reference points.

Why Few-Shot Often Works Better

The examples do several things:

• Define your criteria - What counts as positive vs. neutral isn’t universal
• Set the format - Show exactly how you want the output structured
• Establish tone and style - Demonstrate the voice you’re looking for
• Handle edge cases - Show how to deal with ambiguous situations

How Many Examples Do You Need?

Situation	Examples Needed
Simple, clear task	2-3 examples
Moderate complexity	3-5 examples
Highly specific/nuanced	5-10 examples
Very unusual task	10+ examples

In my experience, 3-5 examples is the sweet spot for most tasks. Beyond that, you get diminishing returns unless the task is genuinely unusual.

Quick Reference: Zero-Shot vs Few-Shot

Use this table to make fast decisions:

Criteria	Zero-Shot	Few-Shot
Speed needed	Fast prototyping	Production quality
Token budget	Constrained	Flexible
Output format	Standard formats	Custom/specific formats
Domain	General knowledge	Specialized/niche
Consistency	Acceptable variance	Must be consistent
Complexity	Simple tasks	Multi-step reasoning
Learning curve	Lower	Higher (crafting examples)

If you’re unsure, start zero-shot and add examples only when you see inconsistent or incorrect outputs.

Zero-Shot vs Few-Shot: Side-by-Side Comparison

Let me walk through the same tasks using both approaches so you can see the difference.

Example 1: Email Classification

Zero-Shot:

Classify this email as: Sales, Support, Spam, or Personal.

Email: "Hi, I noticed your product on LinkedIn. Would you be interested in discussing how our service could boost your sales by 40%?"

The result is usually okay, but the AI might classify LinkedIn outreach as “Personal” or “Sales” depending on interpretation.

Few-Shot:

Classify these emails:

Email: "Hey! Are we still on for lunch tomorrow?"
Category: Personal

Email: "Your order #12345 has shipped and will arrive Tuesday."
Category: Support

Email: "CONGRATULATIONS! You've won a FREE iPhone!"
Category: Spam

Email: "I'd love to schedule a demo of our CRM software."
Category: Sales

Now classify:
Email: "Hi, I noticed your product on LinkedIn. Would you be interested in discussing how our service could boost your sales by 40%?"
Category:

With examples, the AI clearly understands that LinkedIn outreach is “Sales” because it resembles the demo request example.

Example 2: Writing Style Matching

Zero-Shot:

Write a product description for wireless headphones in a casual, fun tone.

You’ll get something reasonable, but “casual, fun” means different things to different models.

Few-Shot:

Here's how we write product descriptions:

Product: Smart Water Bottle
Description: "Stay hydrated, stay awesome. This bottle actually reminds you to drink up—because we all forget sometimes. Plus, it tracks how much you've chugged. Your body will thank you. 💧"

Product: Laptop Stand
Description: "Tired of neck pain? Same. This adjustable stand puts your screen at eye level where it belongs. Looks sleek on your desk, too. Your spine will send a thank you card."

Now write a description for:
Product: Wireless Headphones
Description:

The few-shot approach captures your specific brand voice, emoji usage, and sentence structure.

Example 3: Data Extraction

Zero-Shot:

Extract the company name, founding year, and funding amount from this text:

"TechStartup Inc., founded in 2019, announced today that it has raised $25M in Series B funding led by Venture Capital Partners."

Few-Shot:

Extract information from company announcements:

Text: "CloudCorp (est. 2015) secured $10M in seed funding from Angel Investors LLC."
Extracted:
- Company: CloudCorp
- Founded: 2015
- Funding: $10M

Text: "DataFlow, a 2020 startup, closed a $50M Series A round today."
Extracted:
- Company: DataFlow
- Founded: 2020
- Funding: $50M

Now extract from:
Text: "TechStartup Inc., founded in 2019, announced today that it has raised $25M in Series B funding led by Venture Capital Partners."
Extracted:

The examples establish your exact format and what information to include/exclude.

When to Use Zero-Shot Prompting

Choose zero-shot when:

1. The Task Is Standard and Clear

Translation, summarization, basic Q&A—tasks that AI models have seen millions of times:

Translate to French: "The meeting is scheduled for 3 PM."

No examples needed. The model knows what translation means.

2. You Need Speed

If you’re having a conversation or making quick queries, adding examples every time is impractical:

What are the key differences between TCP and UDP?

3. You’re Exploring

When you’re not sure exactly what you want yet, start with zero-shot to see what the model produces, then refine:

Write a tagline for a coffee shop that emphasizes sustainability.

4. Token Budget Is Limited

Examples consume tokens in API calls. For high-volume applications, zero-shot can be more economical if results are acceptable.

When to Use Few-Shot Prompting

Choose few-shot when:

1. Consistency Matters

If you’re processing many items and need consistent handling:

You're classifying 500 support tickets. Examples ensure "account access issue" and "can't log in" get classified the same way every time.

2. The Task Is Domain-Specific

Industry jargon, specialized formats, or unusual criteria need examples:

Legal document analysis, medical record extraction, or coding in a specific codebase style all benefit from examples.

3. Output Format Is Precise

When you need exact JSON structures, specific naming conventions, or particular formatting:

Show exactly what the output should look like. Words can be ambiguous; examples aren't.

4. Quality Is Critical

For production systems or professional output, few-shot consistently outperforms zero-shot:

Customer-facing content, published writing, or automated systems need the reliability few-shot provides.

Crafting Effective Few-Shot Examples

Not all examples are equally helpful. Here’s how to write good ones:

1. Cover Representative Cases

Include examples that represent the range of inputs you expect:

• Easy case - Clearly fits one category
• Edge case - Could go either way
• Hard case - Requires judgment

2. Show Desired Format Exactly

If you want JSON, show JSON. If you want bullet points, show bullet points:

Input: "John Smith, CEO, Acme Corp"
Output: {"name": "John Smith", "title": "CEO", "company": "Acme Corp"}

3. Include Negative Examples (Sometimes)

For classification tasks, showing what something is not can be valuable:

Review: "It's fine, I guess."
Category: Neutral (NOT Positive—needs enthusiasm for positive)

4. Keep Examples Concise

Long-winded examples waste tokens and can confuse the model. Short, clear examples work best.

5. Order Matters (Slightly)

Some research suggests putting your most relevant examples last, closest to the actual query. But this effect is small—focus on example quality first.

One-Shot Prompting: The Middle Ground

One-shot prompting uses exactly one example. It’s surprisingly effective for many tasks:

Format company announcements like this:

Example:
Before: "Google announces that they will be laying off 12,000 employees effective immediately."
After: "📢 Google | Layoffs | 12,000 employees | Immediate effect"

Now format:
Before: "Microsoft revealed plans to acquire Activision Blizzard for $68.7 billion."
After:

One example is enough when the pattern is clear. Save tokens when you can.

Few-Shot Prompting Best Practices

1. Test Your Examples

Before deploying few-shot prompts, verify:

• Do all examples actually produce correct output?
• Are examples consistent with each other?
• Do edge cases handle properly?

2. Iterate Based on Results

If certain inputs fail, add examples covering those cases:

Original examples didn't handle multi-product orders well → Add a multi-product example

3. Consider Chain-of-Thought

For reasoning tasks, combine few-shot with chain-of-thought by showing reasoning in examples:

Question: Is 17 prime?
Reasoning: I need to check if 17 is divisible by any number from 2 to √17 (about 4.1). 
17 ÷ 2 = 8.5 (not divisible)
17 ÷ 3 = 5.67 (not divisible)
17 ÷ 4 = 4.25 (not divisible)
Answer: Yes, 17 is prime.

Learn more about this in my chain of thought prompting guide.

4. Update Examples as Needs Change

Few-shot prompts aren’t set-and-forget. As your requirements evolve, update examples to match.

FAQ: Zero-Shot and Few-Shot Prompting

Does few-shot prompting work with all AI models?

It works with most modern LLMs (GPT-5, Claude, Gemini, etc.), though larger models generally handle few-shot learning better than smaller ones. Very small models may not have the capacity to learn from examples within a single prompt.

How do I know if I have “enough” examples?

Start with 3 examples and test. If results are inconsistent, add more examples covering the failure cases. If results are already good, you may not need more.

Does few-shot prompting cost more?

Yes—each example consumes tokens, which costs money in API calls. For high-volume applications, balance quality needs against token costs. Sometimes good zero-shot prompts are more economical.

Can I mix zero-shot and few-shot in one system?

Absolutely. Use few-shot for tasks requiring consistency (classification, formatting) and zero-shot for tasks where it works well (simple queries, obvious tasks). This optimizes both quality and cost.

What’s the difference between few-shot and fine-tuning?

Few-shot learning happens at inference time within a single prompt. Fine-tuning actually modifies the model’s weights through training. Fine-tuning is more powerful but requires technical expertise and computational resources.

Making the Choice: A Decision Framework

Here’s how I decide between zero-shot and few-shot:

START
  ↓
Is the task common and well-defined?
  ├─ YES → Try zero-shot first
  │         ↓
  │         Are results consistent and accurate?
  │         ├─ YES → Stick with zero-shot
  │         └─ NO → Add few-shot examples
  │
  └─ NO → Use few-shot from the start
          ↓
          Start with 3 examples
          ↓
          Test with diverse inputs
          ↓
          Add examples for failure cases
          ↓
          Continue until quality is acceptable

When in doubt, try zero-shot first. It’s faster to test, and you might be surprised how well it works. If results aren’t satisfactory, add examples incrementally.

Beyond Basic Few-Shot: Advanced Techniques

Once you’ve mastered zero-shot and few-shot fundamentals, explore these advanced techniques:

Self-consistency prompting generates multiple responses and selects the most frequent answer—ideal for complex reasoning where a single response may be unreliable. See the self-consistency research for details.

Least-to-most prompting breaks complex problems into simpler subproblems, solving them incrementally. This helps when few-shot examples alone can’t capture the full reasoning process. The original research demonstrates impressive results on complex reasoning tasks.

Automatic few-shot selection uses embedding similarity to choose the most relevant examples from a library, rather than hand-picking examples every time.

These techniques build on few-shot foundations—master the basics first. You can also combine few-shot with role prompting for even more control over outputs.

Putting It All Together

The zero-shot vs. few-shot choice comes down to this:

Zero-shot says: “Hey AI, I trust you know what I mean.” Few-shot says: “Hey AI, here’s exactly what I mean—now do the same.”

For everyday queries and exploration, zero-shot is fine. For production systems, consistent outputs, and domain-specific tasks, invest in good few-shot examples.

The effort you put into crafting examples pays off in:

• More consistent results
• Fewer revision cycles
• Better automation reliability
• Clearer communication with the AI

For more prompt engineering techniques, check out my prompt engineering beginner’s guide or explore my prompt library with 200+ templates.

Start experimenting with both approaches. You’ll quickly develop intuition for which works better in different situations—and your AI results will improve dramatically.