Zero-Shot Learning
A model's ability to perform a task it was never explicitly trained on or shown examples of. The model applies its general knowledge and reasoning to handle entirely new task types.
Why It Matters
Zero-shot capability is what makes LLMs so versatile — you can ask them to do things they were never specifically trained for and get reasonable results.
Example
Asking an LLM to classify movie reviews as positive or negative without providing any examples — the model understands the task from the instruction alone.
Think of it like...
Like a multilingual person who has never studied Portuguese but can roughly understand it because they speak Spanish and Italian — related knowledge transfers.
Related Terms
Few-Shot Learning
A technique where a model learns to perform a task from only a few examples provided in the prompt. Instead of training on thousands of examples, the model generalizes from just 2-5 demonstrations.
Transfer Learning
A technique where a model trained on one task is repurposed as the starting point for a model on a different but related task. Instead of training from scratch, you leverage knowledge the model has already acquired.
Generalization
A model's ability to perform well on new, unseen data that was not part of its training set. Generalization is the ultimate goal of machine learning — learning patterns, not memorizing examples.
Prompt Engineering
The practice of designing and optimizing input prompts to get the best possible output from AI models. It involves crafting instructions, providing examples, and structuring queries to guide the model toward desired responses.