NLP (7): Prompt Engineering and In-Context Learning

Fri, 31 Oct 2025 09:00:00 +0000

The same model can produce a sharp answer or a confident hallucination. The difference lies in the framing, not the weights. A vague request like “analyze this text” yields a generic summary; a prompt with a role, two clear examples, and a strict output schema produces something a parser can use. Prompt engineering turns that gap into a repeatable system, not just a lucky shot.

In-Context Learning (ICL) is the mechanism that makes this work. When you include a few examples in the prompt, the model doesn’t retrain; it conditions its forward pass on those examples and effectively infers a task from them. Understanding ICL’s capabilities and limitations separates a developer who struggles with the model from one who guides it.

Prompt Engineering Complete Guide: From Zero to Advanced Optimization

Tue, 30 Sep 2025 09:00:00 +0000

The same model, two prompts: one achieves 17% accuracy on grade-school math, the other 78%. The difference isn’t magic—it’s prompt engineering. This guide covers the techniques that work, the research behind them, and how to systematically optimize prompts for production.

What You Will Learn#

Foundations — zero-shot, few-shot, many-shot, task decomposition, and the five-block prompt skeleton.
Reasoning techniques — Chain-of-Thought, Self-Consistency, Tree of Thoughts, Graph of Thoughts, ReAct.
Automation — Automatic Prompt Engineering (APE), DSPy, LLMLingua compression.
Practical templates — structured output, code generation, data extraction, multi-turn chat.
Evaluation and debugging — metrics, A/B testing, error analysis, the failure-mode toolkit.

Prerequisites. Basic Python; experience calling any LLM API. No math background required.

Prompt Engineering on Chen Kai Blog

NLP (7): Prompt Engineering and In-Context Learning

Prompt Engineering Complete Guide: From Zero to Advanced Optimization

What You Will Learn#