Prompt Engineering, CoT & In-Context Learning

From naive zero-shot queries to systematic Chain-of-Thought (CoT), Tree-of-Thought, and structured JSON schema extraction.

1. In-Context Learning & Few-Shot Prompting

Large models exhibit emergent in-context learning capabilities (Brown et al., 2020) without requiring gradient updates:

\[ P(y \mid x, (x_1, y_1), (x_2, y_2), \dots, (x_k, y_k)) \]

2. Chain-of-Thought (CoT) Reasoning

Direct answering forces the model to compute answers within a single forward pass token. Prompting the model to generate intermediate reasoning tokens ('Let's think step by step') expands the effective computational compute budget allocated per answer.

3. Structured Output & Pydantic Schema Enforcement

Production systems use grammar-constrained decoding (e.g. Outlines, SGLang) to force LLM logits to strictly follow JSON schemas without parsing failures.

🎯 Module Mastery Certification Quiz

+100 XP

Why does Chain-of-Thought (CoT) prompting improve performance on complex multi-step reasoning tasks?

It allows the model to distribute computation across intermediate token generation steps instead of attempting to solve the problem in a single step.

It increases the model's parameter count at runtime.

It disables the softmax temperature sampling.

It forces the GPU clock frequency to increase.