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Master the art and science of communicating effectively with LLMs. Learn zero-shot and few-shot prompting techniques, Chain-of-Thought reasoning that unlocks step-by-step problem solving, system prompts for controlling model behavior, and structured output techniques for reliable integration with downstream systems.
Prompt engineering is the practice of designing inputs that elicit desired outputs from LLMs. It is the primary interface between human intent and model behavior---no code changes, no fine-tuning, just carefully crafted text. Despite its apparent simplicity, effective prompting can dramatically improve model performance on complex tasks.
The core insight of prompt engineering is that LLMs are sensitive to how questions are framed. The same question asked differently can produce answers that vary from incorrect to expert-level. This sensitivity arises because the model's behavior is shaped by patterns in its training data: it responds differently to a casual question than to a formal academic query, differently to a vague instruction than to a precise one.
Modern prompt engineering has evolved from simple instruction-writing to systematic techniques like Chain-of-Thought (CoT) reasoning, which can improve math accuracy by 50%+, and structured output prompting, which enables reliable integration with software systems. Understanding these techniques is essential for any LLM practitioner.
This chapter covers:
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Clear instructions, role assignment, and task decomposition — getting results without any examples.
In-context learning with demonstrations — how example selection and formatting drive model behavior.
Step-by-step reasoning that unlocks math and logic — zero-shot CoT, few-shot CoT, and self-consistency.
Control and reliability for deployed systems
Persona engineering, guardrails, and behavioral constraints — configuring model behavior at the system level.
JSON mode, schema specification, and constrained decoding — reliable machine-readable output from LLMs.
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