Domain Playbooks: Writing, Code, Data & Support — Prompt Engineering Course

What you'll learn

The same principles play out differently in different fields. This module is a set of playbooks— concrete guidance for the domains where prompting is used most: content writing, coding, data work, and customer support. Each shows which techniques from this course matter most for that job.

By the end of this module you'll be able to:

Apply the right techniques for writing, coding, data, and support tasks
Avoid the common failure mode in each domain
Recognize the shared foundation under every playbook

Content & marketing writing

For writing tasks, the levers are role, audience, tone, and format (Modules 6, 9), plus a style example when the voice is specific. The biggest failure mode is generic, on-the-nose output — fixed by naming a precise audience and showing, not telling, the voice you want.

System

You are a senior copywriter. Write in a warm, concrete, jargon-free voice.

Prompt

Write a 2-sentence product hook for a note-taking app, aimed at busy students. Lead with the benefit.

AI response

Capture every lecture idea before it slips away — type, snap a photo, or record, all in one place. When exam week hits, your notes are already organized and searchable, so you study instead of scrambling.

Role + audience + a concrete instruction ('lead with the benefit') turns a generic ask into usable copy.

Coding & code review

For code, context is everything: the language, framework, versions, and the surrounding code. Ground the model in the actual code (Module 18), be specific about the task, and — because models can produce plausible-but-wrong code — always review and test the output. Ask for explanations to make review easier.

Coding task	Prompt focus
Write a function	Specify language, inputs/outputs, edge cases, and style
Debug	Provide the code, the error, and what you expected (Module 27)
Review	Give a rubric: correctness, security, readability, performance
Explain	Ask for a line-by-line or high-level walkthrough at a set level

Coding is a grounding problem: the more relevant code and constraints you provide, the better the result — then verify it.

Data & analysis

Data work leans on structured output and grounding. For extraction and cleaning, demand JSON with a schema (Module 14). For analysis, provide the data (or a sample) and insist conclusions come only from it — and be alert for confidently wrong numbers, which models are prone to. Have it show its work so you can check.

Watch out

Never trust an unverified figure from a model. For real calculations, prefer giving it a tool (Module 23) or having it output the steps you can check — don't rely on mental arithmetic hidden inside prose.

Customer support & chatbots

Support bots live or die by grounding, guardrails, and scope. Ground answers in your real help docs via RAG (Module 19) so the bot doesn't invent policies; set firm guardrails (Module 15) to keep it on topic and refuse gracefully; and escalate to a human for anything sensitive or out of scope (Module 29).

Support need	Technique
Accurate, policy-safe answers	RAG over real docs + 'answer only from context' (Mod 18, 19)
Staying on topic	Scope guardrails + graceful refusal (Mod 15)
Consistent brand voice	System prompt with role and tone (Mod 6)
Handling the hard cases	Escalate to a human; never guess on sensitive issues (Mod 29)

A support bot is a grounding + guardrails problem — exactly the techniques from Phases 4 and 5.

Adapting a playbook to your work

Whatever your domain, the method is the same: identify what the task actually needs, then reach for the matching techniques. Every playbook here is the same foundation — clear instructions, the right context, structured output, grounding, and evaluation — recombined for the job in front of you.

Key idea

There is no separate "magic" per domain. Domain expertise in prompting is knowing which of the core techniques matter most for your task, and applying them deliberately.

Recap & quick check

Key takeaways

Writing: control role, audience, tone, and format; show the voice with an example to avoid generic output.
Coding: ground the model in the actual code and constraints, ask for explanations, and always review and test.
Data: demand structured output, insist conclusions come only from the data, and never trust unverified numbers.
Support: ground answers via RAG, enforce scope guardrails, keep a consistent voice, and escalate hard cases.
Every playbook is the same core techniques recombined — domain skill is knowing which ones matter most.

Quick check

1. What's the most common failure mode in content-writing prompts, and its fix?

2. Why is coding fundamentally a 'grounding' problem?

3. For data analysis, what's the key safeguard?

4. Which techniques matter most for a customer-support bot?

You've now applied prompting across domains. One module remains — a look beyond text, at where the field is heading, and where you go from here. Next up: Module 32 — Multimodal & The Road Ahead.