Prompt Engineering for Business - ChatGPT Copilot LLM AI (Udemy.com)

Prompting Playbook & Frameworks — Write Prompts That Deliver Business-Ready Results (ChatGPT, Gemini, Copilot, Perplexity)

Short, practical, web-based.

Learn the exact frameworks that turn vague asks into clear, reliable outputs across ChatGPT, Google Gemini, Microsoft Copilot (Web), and Perplexity.

No agents. No coding. Just prompts that work.

Why this course now?

AI is already part of everyday work.
Meeting notes, status updates, proposals, personas, job descriptions, variance commentary—tasks that used to take hours can now take minutes.

But there’s a catch.

A basic prompt will give you random, inconsistent responses.
A well-structured, well-scoped prompt produces accurate, predictable, decision-ready results.

This course gives you the playbook for writing those prompts—fast.

What you’ll learn

• How to structure any request with R-C-I-E-S so the model understands exactly what to do.

• How to iterate once or twice with the Draft → Critique → Revise loop and a simple rubric to lift quality without wasting time.

• How to shape prompts with TCR (Task–Context–Rules), plus variables and checklists that you can reuse anywhere.

• How to decompose work into a Chain of Tasks when a single prompt is too vague—no agents required.

• How to ask for bullet caps, word caps, evidence lines, and tables so your outputs are tidy, scannable, and executive-ready.

• How to apply verification habits (no new numbers, assumptions to verify, objections to test) so stakeholders trust the output.

No coding. No installs. Web LLMs only.

Outcomes you can expect

• Turn raw notes into a 110–130-word brief plus 6 tight bullets (≤16 words).

• Convert messy threads into a table with clear owners, dates, and evidence lines.

• Request inclusive JDs, interview questions, onboarding checklists with consistent tone.

• Produce variance bullets that separate structural vs one-time drivers—without inventing numbers.

• Build personas, hooks, landing page blocks, SEO briefs from a simple skeleton.

• Ship work that already looks like your team’s house style: calm tone, bold metrics, one ask, clear date.

Each lesson is short, example-rich, and designed to copy-paste into your tool of choice.

Why these frameworks work

LLMs follow constraints.
When you declare Role, Context, and Inputs, you reduce ambiguity.
When you give Examples and Success Criteria, you define quality in advance.

Iteration beats guessing.
A tight critique rubric directs the model to fix what matters—structure, tone, evidence, word caps—without derailing content.

TCR keeps prompts tidy.
Task clarifies the ask, Context aligns the output to the audience, Rules enforce shape, tone, and verification.

Chains prevent mush.
Complex asks become small steps with clear deliverables.

You keep control without building agents or writing code.

What’s inside each lesson

1) Prompt Anatomy (R-C-I-E-S)

• Role — who the model is acting as (e.g., “You are our FP&A analyst”).

• Context — situation, audience, goals (e.g., “Board prep for Q2, CFO audience”).

• Inputs — the data you actually have (notes, table, JD, policy text).

• Examples — one short good example, one short anti-example.

• Success — format, word caps, bullet caps, evidence, tone, one ask/date.

You get: a reusable skeleton that instantly tightens outputs.

2) Iterative Loop: Draft → Critique → Revise (+ Rubric)

• Run a first draft from your skeleton.

• Apply a critique rubric: structure, clarity, evidence lines, tone, compliance.

• Request a revision that fixes only the misses.

• Optionally add a compression pass (e.g., 120-word brief + 6 bullets).

You get: a repeatable two-pass method that upgrades quality in minutes.

3) TCR Framework + Variables & Checklists

• Task — single verb, clear deliverable (e.g., “Summarize into 6 bullets ≤16 words”).

• Context — audience knowledge, decision level, risk posture.

• Rules — word caps, bullet caps, table columns, no-new-numbers, evidence lines, one ask/date.

• Variables — placeholders like {{audience}}, {{period}}, {{currency}}, {{notes}}.

• Checklists — request owners, due dates, status fields for actionability.

You get: prompts that are easy to reuse and hard to misinterpret.

4) Chain-of-Tasks vs Single-Shot

• When a deliverable feels “mushy,” split it.

• Example chain: extract data → label themes → propose structure → draft brief → verify evidence → compress.

• Each step produces a small, testable output.

• You keep control, the model stays focused, and the result is clean.

You get: a blueprint for complex work that avoids agent overkill.

What this course is (and is not)

Is:

• Web LLMs only (ChatGPT, Gemini, Copilot Web, Perplexity).

• Short, business-ready frameworks.

• Copy-paste templates, examples, and checklists.

• Practical habits for evidence, tone, and verification.

Is not:

• No agents, APIs, or coding.

• No fine-tuning or model internals.

• No long theory.

• No 10–15 hour marathon.

What you’ll be able to do in 60–90 minutes