Real-World Use Cases

Introduction

Before diving into techniques, let's see the impact. The use cases below are all live, in production, and all powered by carefully engineered prompts — not custom models.

Beginner analogy: Like watching pro chefs at work before learning to chop. Seeing what's possible motivates the basics.

Understanding the topic

Core concepts to understand:

• Customer Support — Klarna, Intercom, Zendesk AI Agents.
• Coding — GitHub Copilot, Cursor, Replit Ghostwriter.
• Writing — Grammarly Go, Notion AI, Jasper.
• Search — Perplexity, ChatGPT Search, Brave Leo.
• Tutoring — Khanmigo, Duolingo Roleplay.
• Legal — Harvey AI, Spellbook, Casetext.
• Healthcare — Glass Health, Nabla, DAX Copilot.
• Finance — Bloomberg GPT, Hebbia, Rogo.

Syntax reference

Visual workflow / architecture:

Industry   →  Prompt-powered Feature
─────────────────────────────────────────
Support    →  triage + reply drafting
Code       →  in-IDE completions
Writing    →  rewrite / continue
Search     →  RAG + citation
Education  →  personalised tutoring
Legal      →  clause review
Health     →  scribe + summarisation
Finance    →  research + analysis

Real-world use

Each example above is a public, revenue-generating product. None of them required custom model training — they're prompt engineering on top of frontier models.

Best practices

• Pick a use case close to your domain knowledge — your prompts will be sharper.
• Reverse-engineer leaked system prompts (Apple Intelligence, Cursor) to learn structure.
• Build a tiny version of any of these in a weekend to feel the pattern.

Hands-on exercise

Interview preparation — practice these questions:

• Q1. Name three industries transformed by prompt engineering.
• Q2. Pick a product you use and guess its system prompt structure.
• Q3. Why don't most of these products fine-tune their own model?
• Q4. What does 'production AI' mean to you?