What does few-shot mean in machine learning?

Few-shot refers to a machine learning approach where models learn from a small number of examples (typically 2-10) rather than requiring thousands of labeled training samples. This technique allows models to quickly adapt to new tasks with minimal data.

How is few-shot different from zero-shot and one-shot?

Zero-shot means making predictions with no examples at all, one-shot means learning from a single example, and few-shot means learning from multiple examples but still a small number. Few-shot typically provides better accuracy than zero-shot but requires more data than one-shot.

When should we use few-shot learning in production?

Use few-shot learning when you need to quickly adapt models to new categories or tasks without collecting massive datasets, such as in customer support systems, multilingual applications, or rapid product expansion scenarios.

Is few-shot learning formal or informal terminology?

Few-shot is formal, academic terminology commonly used in research papers, technical documentation, and professional conversations. However, it is also frequently used in casual team discussions among software engineers and data scientists.

few-shot meaning in tech — Speak Tech English

Commonness	4/5
Versatility	4/5
Formality	CasualFormalFormal
Spoken ↔ Written	SpokenWrittenBoth
Directness ↔ Diplomatic	DirectDiplomaticDirect
Region	US and UK

Say it like this

✓We implemented few-shot learning to reduce training time

✓This model uses few-shot adaptation for new tasks

✓Few-shot prompting improved our API response quality

Real examples

in a standup

"We can use few-shot learning here instead of collecting thousands of labeled images for the classification task."

in a code review

"The few-shot approach allows us to fine-tune the language model with just five examples per category."

in a team meeting

"Few-shot prompting with large language models has given us better results than traditional fine-tuning."

Don't say this

Handle with care. Often confused with zero-shot or one-shot learning. Few-shot specifically means using a small number of examples (typically 2-10), not zero and not just one.

✕We need zero-shot learning because we have very few examples.

✓We need few-shot learning because we have a small number of examples to train on.— Zero-shot means no examples at all; few-shot means a small number of examples (typically 2-10).

✕Few-shot learning requires minimal data, so we need only one example per class.

✓Few-shot learning uses a small number of examples per class, typically between 3 and 10, not just one.— One example is one-shot learning, not few-shot. Few-shot requires multiple examples for better pattern recognition.

✕The few-shot model is now trained and will never need updates.

✓The few-shot model can be quickly adapted or retrained with new examples as needed.— Few-shot learning is designed for rapid adaptation, not permanent static models.

Other forms

adjective

few-shot

"We adopted a few-shot learning strategy for rapid model adaptation."

noun phrase

few-shot learning

"Few-shot learning enables models to generalize from minimal training data."

Often used with

few-shot learningThe most common phrase; refers to the complete machine learning paradigm

few-shot promptingUsed with language models to provide examples within prompts for better outputs

few-shot adaptationThe process of quickly adjusting a model using small amounts of new data

few-shot classificationUsing few-shot techniques specifically for classification tasks

Similar words

low-shot learningLess common variant emphasizing the low data requirement

in-context learningOften used with language models; emphasizes learning from context in prompts

Opposites

zero-shotone-shotsupervised learninglarge-scale training

Featured course

English for Developers Bundle

Full tech-English bundle for meetings, code reviews, and everyday developer communication.

500+ reviews

View

Practice

Try it

4 quick exercises

Pick the most natural phrasing

1. Your team wants to quickly classify new product types without waiting for a full training dataset. What approach would you suggest?

We should use few-shot learning to classify these with minimal examples.We need a few-shot learning strategy here.Few-shot learning is the answer to our problem.All of the above are equally natural.

Complete the sentence

2. Our language model improved significantly after we implemented _______ with just three examples per prompt.

Spot the mistake

3. Which sentence uses few-shot incorrectly?

Few-shot learning requires multiple examples but significantly fewer than traditional supervised learning.Since we only have one image of each rare product, zero-shot learning would be better than few-shot.The few-shot adaptation allows our model to quickly handle new customer segments.We implemented few-shot prompting to make our API responses more accurate.

Rewrite naturally

4. Rewrite this using few-shot naturally: 'We can train the model with just a few examples instead of thousands of examples to recognize these new patterns.'

Questions

Quick poll

Have you said "few-shot" in a standup this week?

Want more structured practice like this? Our English for Programmers course covers real workplace English in depth.

Your badges

XLinkedIn

← Back to home