Topic

Few-Shot, Zero-Shot & In-Context Learning

6 posts on few-shot, zero-shot & in-context learning — part of glossary on the n4n AI blog.

Zero-shot vs few-shot vs one-shot prompting

A practical head-to-head comparison of zero-shot, one-shot, and few-shot prompting across capabilities, cost, latency, and ergonomics — with a decision framework for engineers.

July 28, 20267 min read

GlossaryDefinition

What is in-context learning in large language models?

In-context learning lets LLMs adapt to new tasks from examples in the prompt without weight updates. Here's how it works, why it matters, and what engineers get wrong.

July 28, 20266 min read

GlossaryAnalysis

How many examples does few-shot prompting actually need

Engineers need concrete guidance on few-shot example counts — here's what the research and production experience actually show.

July 28, 20266 min read

GlossaryComparison

Few-shot vs fine-tuning: which gets better results

A practical head-to-head comparison of few-shot prompting versus fine-tuning for LLM applications, covering capabilities, cost, latency, and when to use each approach.

July 28, 20265 min read

GlossaryGuide

Few-shot prompting for classification tasks

A practical guide to few-shot prompting for classification tasks — example selection, formatting, ordering, token budgets, and production pitfalls.

July 28, 20265 min read

GlossaryDefinition

Few-shot learning explained: teaching a model by example

A practical guide to few-shot learning for engineers — what it is, how in-context examples steer model behavior, and where it breaks down.

July 28, 20266 min read