Phase 5: Modern LLM Engineering · 75 min · OpenAI API · Anthropic API · DSPy

Prompt Engineering as Interface Design

A prompt is a contract between your product and the model. Version it, test it, and measure it like code.

Hiring signal: Systematic prompt design with measurable outcomes

The Problem

A team ships a chatbot with inline prompts scattered across 40 files. When quality drops, nobody knows which prompt changed. When the product manager asks "why did the tone change?", the engineer says "I tweaked the system message." There is no diff, no test, no rollback path.

Professional prompt engineering treats prompts as versioned interfaces — not as quick strings you iterate on in a playground and paste into code.

The Concept

Prompt Anatomy

Every LLM interaction has a structure:

Your LLM keeps returning responses in the wrong format despite clear instructions. Adding 2 example input/output pairs fixes it. What technique is this and why does it work?

Few-shot examples show the model the exact input→output pattern you want. LLMs are pattern matchers — seeing 2-3 concrete examples of the desired format is more reliable than describing the format in prose, because the model can copy the demonstrated structure directly.

Prompting Strategies (Increasing Complexity)

Strategy	When to Use	Example
Zero-shot	Simple, well-defined tasks	"Classify this email as spam or not spam"
Few-shot	Task needs examples to disambiguate format	Provide 3 input/output pairs
Chain-of-thought	Reasoning tasks, math, logic	"Think step by step before answering"
ReAct	Tasks requiring tool use	"Think → Act → Observe" loop
Self-consistency	When one answer may be wrong	Generate 5 answers, take majority vote

The Prompt Versioning Problem

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You've read the first 2 sections. The rest of this lesson covers Build It, Use It, Eval Harness from Scratch, Ship It, Common Pitfalls, Evaluation, Exercises, Key Terms, Interview Framing — plus a hands-on lab, quiz, and project artifact.

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