Learn Prompting - Prompt Stacking

I finished summarizing Google’s whitepaper which got pretty thorough into prompt engineering. Although AI is now more approachable to the everyday person, this document wasn't.

I broke it down to into real‑work tactics and understandable examples.

If you want to revisit the entire series, you can check out here on my blog.

Now, I'll get into prompt stacking, a technique I know is beneficial to getting the results you want.

Prompt stacking is the habit of chaining multiple prompts together to complete a multi-step task. Instead of asking AI to do everything in one go, you guide it step by step.

You start with one task, see the results, rebuild on it, and keep refining or repurposing the output. This is how professionals, creators, and managers turn AI into a workflow partner instead of a one-off tool.

This is how I usually work with my AI assistants.

Linear Stacking

This is when you move in a straight line: idea to draft to refine to repurpose. You stay in the same discussion thread to help the AI remember context and respond with more relevance.

Why it works:
Each step builds on the previous one.

When to use it:

• When you want to save time by building on earlier outputs

• When you’re working toward one clear outcome

​

Branch Stacking

Start with one prompt, then create different outputs from it. This might mean reformatting a single idea for multiple audiences.

Why it works:
You reuse one solid idea and create multiple deliverables from it. This keeps things fast, focused, and consistent.

When to use it:

• When you're speaking to different people or platforms

• When you're repurposing content

• When you're managing parallel outputs from one starting point

Example 1: Team Update from a Manager (Linear)

• “Summarize the key accomplishments from this sprint using my notes below. Keep it brief.”

• “Now make it sound less technical. It’s going to stakeholders.”

• “Turn this into three bullet points for the executive summary.”

Each follow-up builds directly on the last. No need to reintroduce the topic or clarify your ask. Stay in the same thread and keep building.

Example 2: Creator Writing a Newsletter (Linear to Branch)

• “Based on these bullet points, write a casual newsletter intro for busy professionals.”

• “Pull out a single sentence to use as a hook for LinkedIn.”

• “Turn the rest of this into a blog post.”

One idea, multiple formats. You’re moving efficiently and getting consistent tone.

Example 3: Strategist Organizing Research (Linear)

• “Summarize these customer interviews and highlight recurring themes.”

• “Group the themes into three categories with bullet points.”

• “Create a presentation outline based on these categories.”

You’re not asking the AI to do it all at once. You’re layering value at each step.

Example 4: Entrepreneur Planning a Course (Branch)

• “Based on this course idea, outline three modules with three lessons each.”

• “Draft a talking head script for Module 1 in my tone.”

• “Pull out three social tips based on this module.”

Same foundation. Multiple directions.

Example 1: Team Update from a Manager (Linear)

Each new prompt builds on the last if you stay in the same chat window. That means better results, less repetition, and fewer instructions to retype.

If you start a new thread every time, you're forcing the AI to relearn what you just taught it. That’s like holding a team meeting, then leaving and starting over every time someone asks a question.

In most AI tools, it will assign a short title to the thread based upon the commonly used terms and topic. And again, in most AI tools, it will give you the opportunity to rename it.

Send me an email with your prompt. I'm happy to provide feedback. I reply to each and every email.