Learning path map
Detailed content
🔄 Think in systems, not commands
From a one-off request to a workflow that works without relying on your memory. You learn to see AI as one part of a process — not a standalone oracle — and to sketch that process on paper.
A workflow is a sequence of steps where the output of one becomes the input of the next. Instead of making isolated requests, you map out what goes in, what the AI does, what comes out, how you evaluate it, and what you adjust.
With a designed workflow, anyone (or you tomorrow) can carry out the entire process without relying on memory. The work starts flowing instead of getting stuck.
Workflow, linked steps, input, output, memory dependency eliminated.
AI can take on different roles within a workflow: at one point it interprets raw input; next it generates a proposal; then it evaluates its own output against criteria you defined. Each role is a box in your design.
Understanding the possible roles helps you avoid asking for everything at once in one giant prompt. A well-designed system distributes responsibilities across manageable steps.
AI roles, process box, interpretation, generation, evaluation, partial execution.
The exercise of taking a real process (creating a post, writing an analysis, putting together a lesson) and drawing the boxes on paper: what goes in, what each box does, and what comes out of each one.
Writing it down reveals steps you were doing mentally without realizing it—and bottlenecks that were previously invisible. The diagram is the first version of the system.
Workflow diagrammed, process boxes, visual map, bottleneck revealed.
Not every process is linear. A decision point is where the workflow can take different paths based on a criterion: “approved → deliver; rejected → revise.” These points are drawn as diamonds or branches.
Mapping the branching paths turns the implicit logic in your head into an explicit system rule — any executor (human or AI) knows what to do in each case.
Decision point, condition, branch, explicit criterion, system logic.
Every system has a handoff: the moment when AI passes the baton to a human (approve, adjust, publish, decide) and vice versa. Explicitly marking these points is what distinguishes a real workflow from an illusion of automation.
Knowing where humans are irreplaceable helps you avoid two opposite mistakes: automating what requires human judgment and manually doing what AI could do in seconds.
Handoff, human approval point, accountability, judgment vs. execution.
The fundamental cycle of any intelligent system: you define criteria (Think), AI acts (Act), the result is checked against the criteria (Evaluate), anything that falls short is adjusted (Adjust), and the cycle starts again (Act).
Without the loop, the system runs once and stops. With the loop, the system improves on its own until it meets the criteria—which is the essence of intelligent work.
Loop, improvement cycle, quality criterion, systemic iteration, feedback arrow.
A high-level view (with no obligation to use it) of how more complex systems can be built: no-code tools like n8n, Make, Zapier, and the concept of AI agents. None of them are required—the course is 100% doable in any chat.
Knowing the horizon without depending on it means that, when the right time comes, the transition will be natural. Understanding the concept helps you learn the tool 10x faster.
No-code, AI agent, automation, long-term vision, natural system progression.
⚙️ Building a mini system
From a paper workflow to an executable workflow in a simple chat. You learn to chain prompts (the output of one becomes the input of the next), create review loops, and build the minimum 3-box system that already delivers value — in any profession.
Each box in your workflow diagram becomes a separate prompt in an AI conversation. The sequence of messages in the same session runs the system — no installation or setup required.
The barrier to entry is zero. Anyone with access to an AI chat can operate the system the same day they learn the concept.
Executable workflow, chat as a system, sequential prompts, zero setup.
The technique of taking the result of one prompt and using it as raw material for the next. Complete 4-step example: (1) brief → (2) structure → (3) copy → (4) review. Each output feeds the next input.
It’s the central piece of no-code architecture. Mastering the sequence means mastering 80% of what sophisticated systems do — right inside a chat, right now, with no dependence on a tool.
Chaining, output → input, prompt pipeline, 4 complete steps.
The T3 baseline brief, T4 playbooks, and T1 prompt libraries are pieces that feed the system. Instead of re-explaining the context in every session, you feed it in as the first input in the workflow.
The earlier layers of the pyramid (T1 to T4) become active within the system. This is where everything connects—architecture uses prompts, context, and methods as building blocks.
Knowledge base, context injection, asset reuse, connected pyramid.
An evaluation prompt that takes the generated result and compares it against a rubric you wrote: "Analyze this text according to criteria X, Y, and Z. Point out what didn’t meet the standard and why." The loop closes when all the criteria are met.
Transforms the system from passive (runs once) to active (evaluates and improves). This is what sets an intelligent workflow apart from a sequence of copy-and-pastes.
Review loop, guided self-assessment, quality rubric, active system.
Before the system runs, you explicitly write down what "good" means for that result: length, tone, required elements, and what must not appear. These criteria inform both the generation prompt and the evaluation prompt.
Without explicit criteria, evaluation is arbitrary ("I don’t like it") and the system gets stuck in an infinite loop. With clear criteria, the system converges—and you can delegate without losing the standard.
Explicit criterion, rubric, quality standard, system convergence.
The simplest system that works: [Input + context] → [AI generates a proposal] → [AI evaluates it against criteria → delivers or revises]. Three boxes. Ready to use today in chat.
Perfect systems never get off the ground. The minimum system starts working now and evolves with use—exactly what T6 (Evolution) will systematize.
Minimum viable system, 3 boxes, process MVP, start now, evolve later.
The mini-system applied to 5 profiles: teacher (idea → ready-to-teach lesson workflow), lawyer (contract analysis workflow), salesperson (lead → proposal workflow), manager (weekly decision workflow), creator (idea → published post workflow). Each workflow in 3 boxes.
Seeing the system in your specific context turns a concept into action. You leave with a draft of your own workflow—not a generic one.
Industry-specific application, workflow by profession, concrete context, outline of your system.