4.5 Prompting and In-Context Learning¶

Role at Stage 4: Large Language Models¶

Use instructions, examples, constraints, and decomposition before heavier adaptation. This part is one capability inside the stage. It should leave behind an artifact, measurements, and a short explanation of failure modes.

Explanation¶

This part has 4 sub-parts because the topic needs that many learning units to feel natural. Some stages have more parts and some have fewer; the structure follows the topic, not a fixed template.

Part Diagram¶

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flowchart LR
  P["<b>4.5</b><br/>Prompting and<br/>In-Context Learning"]
  P --> S1["<b>4.5.1</b><br/>Prompt Anatomy"]
  P --> S2["<b>4.5.2</b><br/>Zero Shot Few Shot and<br/>Examples"]
  P --> S3["<b>4.5.3</b><br/>Task Decomposition"]
  P --> S4["<b>4.5.4</b><br/>Prompt Versioning and<br/>Tests"]
  P --> E["<b>Exam</b><br/>Part practice"]

Sub-Parts¶

Sub-part folder	What it explains
4.5.1 Prompt Anatomy	Prompt Anatomy 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.
4.5.2 Zero Shot Few Shot and Examples	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.
4.5.3 Task Decomposition	Task Decomposition 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.
4.5.4 Prompt Versioning and Tests	Prompt Versioning and Tests 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.

What a Person Who Masters This Part Can Do¶

Explain how Prompting and In-Context Learning supports an llm fundamentals notebook comparing models, tokenization, structured outputs, embeddings, costs, and failure cases..
Build and inspect this artifact: Build a prompt testing lab.
Measure progress with: Track prompt version, pass rate, output validity, and regression cases.
Debug at least one failure mode before moving to the next part.

Build and Measure¶

Build: Build a prompt testing lab.

Measure: Track prompt version, pass rate, output validity, and regression cases.

Tests¶

Take one 30-question exam after studying this part. It opens in a new browser tab so the study page stays available.

Open Part Exam

Back to Stage¶

Return to Stage 4: Large Language Models.