4.5.2 Zero Shot Few Shot and Examples

Why This Sub-Part Matters

Zero Shot Few Shot and Examples is the working skill inside Prompting and In-Context Learning that helps you build the stage artifact, An LLM fundamentals notebook comparing models, tokenization, structured outputs, embeddings, costs, and failure cases, while collecting enough evidence to trust the result. A sub-part is now a folder so longer topics can grow without forcing everything into one huge page.

Study Pages

Page	Purpose
Deep Dive	Full explanation, mechanisms, examples, and failure modes.
Examples and Practice	Worked exercises, project drills, and self-check prompts.

Core Ideas

• Define Zero Shot Few Shot and Examples in plain language before naming tools or frameworks.
• Connect it to the stage artifact: An LLM fundamentals notebook comparing models, tokenization, structured outputs, embeddings, costs, and failure cases.
• Measure it with: prompt version, pass rate, output validity, and regression cases
• Name at least one failure mode, because real AI engineering is mostly controlled failure reduction.
• Keep the first implementation small enough to inspect by hand before scaling it.

How to Study It

1. Read this overview and write the concept in your own words.
2. Read the deep dive and identify the input, transformation, output, and failure mode.
3. Complete the examples and practice page.
4. Add one measurement using: Track prompt version, pass rate, output validity, and regression cases.

Completion Standard

• I can explain Zero Shot Few Shot and Examples without naming a tool first.
• I can connect it to the stage artifact.
• I can show a small artifact, measurement, or test.
• I know how it fails and what I would inspect first.

Return to 4.5 Prompting and In-Context Learning.