Prompting
Few-Shot Learning
A prompting technique where you include a small number of input-output examples in the prompt to show the model the pattern you want it to follow.
In depth
A prompting technique where you include a small number of input-output examples in the prompt to show the model the pattern you want it to follow. Instead of describing the format in words, you demonstrate it with 2-5 examples. Few-shot learning reliably improves output quality for classification, formatting, and domain-specific tasks without any model training.
Example
In practice, developers reach for Few-Shot Learning when they need the capability described above as part of an AI feature or workflow.
Go deeper at Developers Digest
Hands-on guides, comparisons, and tutorials that cover Prompting.
FAQ
What is Few-Shot Learning?
A prompting technique where you include a small number of input-output examples in the prompt to show the model the pattern you want it to follow.
Why does Few-Shot Learning matter for AI developers?
Few-Shot Learning sits in the Prompting part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Where can I learn more about Few-Shot Learning?
Developers Digest publishes tutorials and videos that cover Prompting topics including Few-Shot Learning. Check the blog and YouTube channel for hands-on walkthroughs.
Related terms
Prompting
Zero-Shot Learning
A model's ability to perform a task it was not explicitly trained on, using only the instructions in the prompt with no examples.
Prompting
Prompt Injection
An attack where malicious input tricks an AI model into ignoring its instructions and following attacker-supplied commands instead.
Prompting
Chain of Thought (CoT)
A prompting technique where the model is asked to show its step-by-step reasoning before arriving at a final answer.
Get Smarter About AI Dev
New tutorials, open-source projects, and deep dives on coding agents - delivered weekly.
One email per weekReal code, not theoryFree forever