A Practical AI Workflow: Making AI Do the Heavy Lifting

A Practical AI Workflow: Making AI Do the Heavy Lifting#

The chapters in this book teach the mechanics of generative AI. This chapter is about habit, a repeatable personal workflow for getting consistent, high-quality output from everyday AI assistants. It is adapted, with credit, from a widely shared LinkedIn post by Dan Martell describing how his team pushed AI to handle the bulk of their routine work.

Credit and framing

This workflow is adapted from a public LinkedIn post by Dan Martell (founder, author of Buy Back Your Time). The original frames the goal as having AI do “92% of the work.” Treat that figure as motivational shorthand, not a measured benchmark; no peer-reviewed evidence supports a specific percentage. The steps below are summarized and connected to the concepts in this book; the commentary is original. Always keep sensitive data out of consumer AI tools (see AI Literacy and Responsible Use).

The idea in one line#

Stop treating AI as a chatbot you re-explain yourself to every time. Instead, build reusable context and instructions once, then reuse them so the AI works like a trained team member. The split the post proposes is useful: let AI handle research, drafts, analysis, and options; keep vision, taste, final decisions, and emotional intelligence human.

The seven steps#

1. Get a capable assistant#

Start with a capable, paid-tier assistant (the post uses ChatGPT Plus/Pro; Claude and Gemini have equivalents). Capability and larger context windows matter for this workflow. Remember the privacy guidance in AI Literacy and Responsible Use: use an appropriate tier and keep regulated data out.

2. Create a master prompt (your context)#

A master prompt captures who you are so the AI starts every task already informed. The post’s method: ask the AI, “I’m [role] at [company type]. Create a master prompt for me. Ask me every question you need to give the most context possible,” spend 30-45 minutes answering, save the result, and paste it into new chats. In this book’s terms, this is the context component of a prompt (Chapter 3: Prompt Engineering), captured once and reused.

3. Build system prompts (your formula)#

If a master prompt tells AI who you are, a system prompt tells it how to work. The post’s trick is reverse-engineering: get the AI to produce a great output through 3-6 iterations, then ask, “Write the system prompt that would have generated this output,” and save it. You now have a reusable formula for that quality, an applied form of prompt engineering and instruction design.

4. Use project folders (persistent context)#

Group related work into projects (or “folders”), each loaded with the master prompt and relevant documents, so every conversation builds on the same context and can be shared with a team. Conceptually this is memory and retrieval applied to your own knowledge, the same idea formalized as RAG in Chapter 3: Retrieval-Augmented Generation.

5. Set custom instructions (your defaults)#

Use the assistant’s custom instructions / personalization settings to fix your communication style once, short, bulleted, no fluff, preferred tone, and to ban filler words like “delve” and “moreover.” This stops you repeating formatting requests and is the personal-settings analogue of a standing system prompt.

6. Turn workflows into custom assistants#

Package your best system prompts into reusable custom GPTs / assistants, one per repeatable task (email, content, financial analysis, hiring, strategy). Update the assistant once and everyone who uses it benefits. This is a no-code version of the agent and tool patterns in Chapter 4: Agents: a specialized, reusable worker for a specific job.

7. Use AI to improve your AI#

Ask the assistant to help build the workflow itself, to draft your master prompt, write system prompts, suggest custom instructions, and improve your prompts. This is the iterative, “prompting is a loop” mindset from Chapter 3: Prompt Engineering, turned on the workflow itself.

What “AI does the work” looks like in practice#

Area

AI does

You do

Content

Research, outlines, first drafts.

Edit and add your voice.

Operations

Draft SOPs, analyze processes, suggest improvements.

Decide.

Finance

Analyze reports, build models, surface insights.

Make the decisions.

Strategy

Process information, lay out options.

Choose the direction.

The consistent pattern: AI drafts and analyzes; humans judge and decide. The human 8%, vision, taste, final decisions, and emotional intelligence, is exactly the part that responsible-AI practice (Module 2) says should stay human.

How this maps to the rest of the book#

This workflow is the everyday, no-code expression of ideas you will see formalized later:

Building a reusable prompt template library#

The workflow above produces reusable prompts; the next skill is organizing them into a template library so you never start from scratch. The following eight-step system is adapted, with credit, from a LinkedIn post by Ronnie Parsons (Mighty AI Lab).

Credit

Adapted from a public LinkedIn post by Ronnie Parsons (https://www.linkedin.com/in/ronnieparsons). The steps are summarized; the commentary and example are original to this book.

  1. Identify high-value AI tasks. Track which prompts you write repeatedly (client research, content creation), note how often you use each, and estimate the time spent rewriting similar prompts.

  2. Analyze prompt structure. Review 3-5 prompts that worked well, highlight the parts that stay consistent, and circle the information that changes each time.

  3. Create clear variables. Replace the changing elements with descriptive variables in [BRACKETS], using ALL_CAPS names (for example [CLIENT_NAME]) that clearly indicate what goes there.

  4. Build a template framework. Start with the AI role/context, give clear output instructions, structure the body with numbered sections, and end with specific formatting instructions, the prompt components from Chapter 3: Prompt Engineering, made reusable.

  5. Test with multiple inputs. Run the template with three different sets of variable data, compare outputs for consistency and quality, and refine the wording to remove inconsistencies, the iterative loop of prompt engineering.

  6. Organize the template library. Keep templates in a dedicated document, grouped by business function (marketing, operations), with usage notes for each.

  7. Develop template workflows. Identify templates that work together and create “connectors” where one template’s output feeds the next, an informal version of the chains in Chapter 1: LangChain Modules.

  8. Improve continuously. Schedule weekly reviews, track time saved and quality, and refine templates from feedback, the LLMOps mindset from Chapter 1: Evaluating LLMs.

Pro tip: variable defaults

Include a default example inside each variable, [VARIABLE_NAME="Example value"]. This shows what kind of input works best and lets you leave the default in place for values you use often, reducing decision fatigue. For example:

[TONE="Professional and authoritative, but approachable"]

A template, in practice, looks like this, and is trivial to fill programmatically:

TEMPLATE = """You are a [ROLE="senior market analyst"].
Task: Research [COMPANY_NAME] and summarize in [SECTIONS="3"] numbered sections.
Tone: [TONE="Professional and authoritative, but approachable"].
Format: bullet points, no preamble."""

def fill(template, **values):
    import re
    # Replace [NAME] or [NAME="default"] with the provided value or the default.
    def repl(m):
        name = m.group(1)
        default = m.group(2)
        return values.get(name, default if default is not None else f"[{name}]")
    return re.sub(r'\[([A-Z_]+)(?:="([^"]*)")?\]', repl, template)

print(fill(TEMPLATE, COMPANY_NAME="Acme Corp"))   # uses defaults for the rest

Together, the master-prompt workflow and this template library turn ad-hoc prompting into a maintainable system, the practical foundation that the prompt engineering, chains, and evaluation chapters formalize.

Key takeaways#

  • Build reusable context (master prompt), reusable formulas (system prompts), and reusable workers (custom assistants), then stop re-explaining yourself.

  • Use project folders and custom instructions so context and style persist.

  • Let AI draft and analyze; keep judgment, taste, and final decisions human.

  • These habits are the practical, no-code front end to the prompting, retrieval, and agent concepts taught throughout this book.

Source

Adapted with credit from a public LinkedIn post by Dan Martell (https://www.linkedin.com/in/dmartell). The original text is the author’s; the summary, commentary, and cross-references here are original to this book.