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Zero-Shot and Few-Shot Prompting

Two fundamental prompting techniques are zero‑shot (no examples) and few‑shot (provide a few examples). Few‑shot often yields more consistent, formatted responses.

Zero‑Shot Prompting

You give only the instruction, no examples. The model relies on its training.
Classify the sentiment of this review: "The product is amazing!"
Output: Positive
Works well for common tasks but may fail for complex or domain‑specific ones.

Few‑Shot Prompting (In‑Context Learning)

You provide 2‑5 examples of input‑output pairs before asking the real question. The model learns the pattern from examples.
Review: "Great battery life." → Sentiment: Positive
Review: "Cheap build quality." → Sentiment: Negative
Review: "The screen is decent." → Sentiment: Neutral
Review: "Absolutely love this phone." → Sentiment:
Few‑shot improves accuracy and consistency, especially for formatting.

When to Use Each

  • Zero‑shot: Simple tasks, quick prototyping, low cost.
  • Few‑shot: Complex tasks, specific output formats, domain adaptation.


Two Minute Drill
  • Zero‑shot: no examples – model uses general knowledge.
  • Few‑shot: provide 2‑5 examples to guide the model.
  • Few‑shot improves consistency and format adherence.
  • Also called in‑context learning.

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