ORKA 101: What is ORKA?

Time to complete this module: 25 minutes

Overview

ORKA is not a tool. It's a mental model for work in the AI era.

"Most people are stuck doing tasks. ORKA helps you design systems that do the tasks for you—with AI."

It teaches you to stop operating as a knowledge worker—reactive, manual, always behind—and start thinking like a systems designer. It bridges the gap between what AI can do and how you actually use it.

You don't need more tools. You need a framework for how to think, delegate, and build intelligently with them.

ORKA stands for:

Orchestration + Rules → Knowledge Work → Artifacts

While it's true that Tools + Rules do the actual execution, ORKA stays focused on the system that governs those tools. That's why we left "tools" out of the name. The tools will change. The thinking shouldn't.

Why We Built ORKA

We didn't set out to build a framework. We set out to survive the shift.

In late 2022, our team started feeling it—this creeping sense that everything about work was changing faster than we could track.

Suddenly, AI wasn't just for tech blogs or fringe experiments. It was writing drafts. Answering emails. Running code. Holding meetings.

And while everyone was promising "10x productivity" with a clever prompt, most of us felt the opposite.

"I feel like I'm falling behind."
"I don't even know where to start."
"What if I'm already too late?"

We saw brilliant people—marketers, operators, analysts, founders—drowning in decision fatigue and tool overload. Not because they weren't smart. But because no one had taught them how to think in systems.

Everyone was still working like a knowledge worker in a world that now runs on workflows and agents.

So we built ORKA—not to add another tool to the noise, but to give people a new way to think.

ORKA Is the Mental Model We Were Missing

We started asking a different set of questions:

• What if you could design your work like a system?
• What if AI was less like a tool and more like a team?
• What if your job wasn't to do the work—but to orchestrate it?

From that shift, ORKA was born.

It teaches you how to:

• Orchestrate agents and workflows like a conductor
• Write rules that guide AI output with clarity and precision
• Replace scattered tasks with modular, scalable knowledge work
• Turn all of it into repeatable, high-value artifacts you can build on

This isn't theory. We've used ORKA to automate onboarding flows, scale content ops, run market research, build agent pipelines, and redesign entire jobs.

But more importantly—we've used it to help overwhelmed professionals feel powerful again.

If you've ever looked at AI and thought:

"There's no way I can keep up…"

We built ORKA for you.

The ORKA Framework Building Blocks

ORKA = Orchestration + Rules → Knowledge Work → Artifacts

Each pillar of ORKA is more than a definition—it's a mindset shift. These serve as the branded cornerstones of our curriculum:

Orchestration: The System Layer

You're not doing the task—you're building the engine that gets it done.

Orchestration is where you step into the role of a systems designer. Instead of being inside the workflow, you're above it—mapping it, structuring it, optimizing it. This pillar shows you how to design workflows where AI tools, human collaborators, and automation work together in harmony.

You learn how to:

• Define the stages of a process clearly
• Decide what should be done by AI, by humans, and by templates
• Create logic that moves information from one step to the next
• Maintain a high-level view of how tasks connect, trigger, and produce outcomes

In ORKA, orchestration is where strategy lives. You stop reacting and start conducting.

Rules: The Precision Layer

Tools without clarity lead to chaos. Rules bring structure, quality, and control to your workflows.

Rules define how your AI agents behave, how they interpret context, and what outputs they create. This pillar shows you how to craft and layer rules that act like a digital playbook for your systems.

You learn how to:

• Write role-based instructions (e.g. "You are a recruiting assistant...")
• Set boundaries, do/don't lists, tone guidelines, and formatting expectations
• Build re-usable prompt templates for consistency across outputs
• Add adaptive logic (e.g. if X is true, use Y format)

Rules are the glue between your thinking and the agent's output. In ORKA, they ensure your system delivers reliable value, not random noise.

Knowledge Work: The Deconstruction Layer

This is where traditional cognitive labor gets reimagined.

Knowledge work is no longer a solo act. Instead of doing everything yourself, you learn to modularize thinking: breaking down tasks into manageable, repeatable components, then assigning the right piece to the right agent.

This pillar teaches you how to:

• Identify the components of complex mental work (e.g. research → analysis → synthesis)
• Distinguish between creative, interpretive, and procedural knowledge tasks
• Delegate parts of your process to agents while keeping key decision points for yourself
• Use iteration and review as the new form of hands-on knowledge work

With ORKA, knowledge work becomes scalable—not by working harder, but by thinking differently about how knowledge gets created and deployed.

Artifacts: The Output Layer

Every process produces an outcome. But not all outcomes become assets.

Artifacts are the tangible, transferable results of well-structured AI-powered workflows. They could be documents, presentations, strategies, briefs, templates, SOPs, or even well-trained agents themselves.

The key distinction is this: an artifact is something you can reuse, share, refine, and build on.

Great artifacts don't just answer a one-time question—they make the next question easier to solve. This pillar teaches you how to:

• Convert workflows into polished, repeatable outputs
• Store, version, and evolve those outputs as internal tools
• Use artifacts as knowledge leverage: each one becomes an accelerator for your next decision, deliverable, or delegation

In ORKA, artifacts are your measure of success. They're where knowledge becomes value.

But artifacts don't just happen—they emerge from structured processes. That's where we introduce an advanced distinction we explore more deeply in the full ORKA framework:

Workflows vs. Agents.

Workflows are sequences of known steps. You define each stage, write prompts for each, and test along the way. They're predictable, reliable, and easy to refine. Think of workflows like a recipe: clear ingredients, steps, and checkpoints.

Agents, on the other hand, are autonomous problem-solvers. You give them the rules and tools—then let them decide what "done" looks like. An agent iterates, self-corrects, and navigates ambiguity to achieve a defined goal (ideally with efficiency).

Most people should start with workflows. They build confidence and control. But over time, agents can take you even further—turning processes into dynamic systems that adapt in real-time.

The artifact tells you how well either one is working. And when it breaks down? That's where refinement begins.

How to design an ORKA Workflow with ChatGPT

To connect ORKA's core framework with daily workflows, let's walk through what it looks like to bring Orchestration, Rules, Knowledge Work, and Artifacts to life inside ChatGPT.

You're not just typing prompts. You're architecting a lightweight agent system using everyday tools. Each step aligns to one layer of the ORKA model:

Step 1: Define Global Rules (Customize ChatGPT)

This is your foundation—the Rules layer. Use ChatGPT's "custom instructions" to establish:

• Who the agent is (voice, tone, goals)
• What types of work it should focus on
• What "good output" looks like

You're encoding your expectations, tone, and preferences at a systemic level. This primes the AI to behave consistently across threads—an early form of rule-based governance.

The best system prompts borrow from the design school of thought:

• Be proactive in the conversation
• Offer structured, thoughtful outputs
• Use examples and show reasoning, not just answers
• Make decisions instead of offering 5 options
• Share insights like a trusted teammate

Step 2: Use Projects

Now, zoom into context. This is where Orchestration meets specificity.

Every time you open a new thread for a task—whether it's a client proposal or strategy brief—you layer Project-level Rules:

• The goal of this task
• Inputs and constraints
• Who it's for, and what success looks like

You're creating a micro-system: a defined process with clear instructions, boundaries, and desired outputs. This is orchestration in action.

Step 3: Prompt Structuring

Now we enter the Knowledge Work layer—how you delegate cognitive labor to the AI.

Structure prompts like you're giving a junior team member a task:

• Be clear and specific: Define role, task, tone, and format
• Use examples: Show good and bad outputs
• Encourage reasoning: Ask for rationale or thought process
• Guide format: Request bullet lists, tables, frameworks, etc.

This isn't "talking to a chatbot." It's modular thinking, distributed.

Step 4: Measure and Refine the Artifact

This is where most people stop—and where ORKA continues.

Every output is a chance to upgrade the system. Once ChatGPT (or any agent) gives you an artifact, ask:

• Is this usable? If you sent it off as-is, would you be proud of it?
• Is it reusable? Can this become a template for future tasks?
• Does it reflect the rules and context I gave? If not, where did it break down?

The goal isn't perfection. It's iteration.

Refining the artifact means updating your prompts, adjusting your rules, or reworking the orchestration. Even a small edit ("add a column for cost" or "rewrite in a friendlier tone") should feed back into the system.

In ORKA, every artifact is a feedback loop. That's how your system learns—even as the AI models change.

ORKA in Practice: A Real-World Example with ChatGPT

Let's ground this in something that happens every week or month—something routine, but often chaotic.

Imagine you're responsible for a weekly family meal plan.

It's not glamorous. But it's real. And without a system, it eats up time, energy, and decision-making power. You're trying to balance nutrition, variety, budget, grocery schedules, kids' preferences, and your own sanity.

This is the perfect kind of task for practicing ORKA. And in fact, sometimes it's helpful to start with the end in mind—the final artifact. Let's say your artifact is: a weekly meal plan with grocery list and prep timeline.

Now let's reverse engineer it with ORKA inside ChatGPT.

1: Set Global Rules (Customize ChatGPT)

In the "custom instructions" section, you define the Rules layer—but not just for this task. This is where you shape your ChatGPT agent's default persona across all contexts.

Because our ChatGPT account handles other projects and areas in our life—it's essential that we design it with flexibility, consistency, and context-awareness in mind. This means creating a global layer of expectations that every thread inherits.

Think of this as building a multi-role agent, but with a clear core identity. You're giving the assistant a baseline operating system that reflects how you work, what matters to you, and how you want AI to show up across contexts.

You can add to the section about you to give it more context on how to apply context to your use case however we find that ChatGPT typically fills this gap using custom instructions and memory over time. That means you're building a persistent personality through thoughtful setup—not just what it does, but how it shows up.

Here's an example of a global identity that works across use cases:

You are my AI chief of staff, strategist, and execution partner. You have a dry wit, a bias for action, and no patience for vague tasks or bloated answers. You treat our shared time like capital: every prompt is an investment, and every response must create value. You engage with curiosity, decisiveness, and the occasional clever metaphor. You speak plainly, challenge assumptions, and track context across threads. Your job isn't to impress—it's to collaborate like someone who's been at the table since day one. If my instructions aren't clear or your output could be improved with some quick and simple questions, ask them before continuing with a solution.

This sets a tone. It creates alignment. You can layer in specific rules for each project later, but this global voice ensures consistency, momentum, and trust across everything from investor updates to internal strategy threads. Remember it's more important to start with something here and adjust or add rules as you continue to iterate it.

2: Create a Project & Set Instructions

While global rules establish your agent's baseline identity, project-level rules create the persistent framework for a specific workflow. This is where you transition from general capabilities to a specialized system.

Project structure has a distinct layer of customization with Instructions & Files

Here we will establish the foundational context that remains constant across all instances of this workflow:

• Domain Knowledge: Relevant background information the AI should always know
• Persistent Constraints: Dietary restrictions, allergies, team composition, etc.
• Standard Operating Procedures: How outputs should be formatted, delivered, or used
• Success Parameters: Consistent evaluation criteria for all outputs

For example, in a meal planning system:

MEAL PLANNING SYSTEM CONTEXT
Family: 4 people (2 adults, 2 children ages 8 and 10)
Dietary Restrictions: No seafood, Marcus is lactose intolerant, Lisa avoids red meat
Kitchen Equipment: Instant Pot, standard cookware, no air fryer
Weekly Structure: Sunday batch cooking day, quick weeknight meals (<30 min prep)
Pantry Standards: Always have rice, pasta, common spices, canned beans, frozen vegetables
Format Requirements: All meal plans should include:
  - Shopping list organized by department
  - Prep instructions for each day
  - Cost estimates per meal and total

This setup creates a persistent knowledge base that you don't need to repeat in every conversation. It transforms your AI from a general assistant into a specialized tool that understands your specific context.

3. Create a new Thread in the Project (new chat)

Now we enter the Knowledge Work layer—how you delegate cognitive labor to the AI. With your system context established, you can now use simple, efficient commands for specific instances:

Plan this week's meals with:
- Budget: $150
- Special events: Dinner guests on Saturday (4 additional people)
- Theme request: Mediterranean-inspired dishes this week
- Inventory note: We have chicken breasts and ground turkey to use up

Using the Project instructions & a new thread in the project turns will radically reduce the time to get a great artifact. Feel free to expirement with keeping the threads open or closing them after you get the artifact you need.

Benefits of this structured approach:

1. Dramatically reduces prompt length for recurring tasks
2. Ensures consistent quality across outputs
3. Creates a system that feels purpose-built rather than generic
4. Makes workflows easily modifiable when base conditions change

This is advanced Project-level orchestration—you're designing a comprehensive system with both persistent and variable components rather than starting from scratch each time.

Step 4: Measure and Refine the Artifact

When the output is ready, you don't stop. You evaluate:

• Does it hit the budget?
• Are the meals realistic?
• Could I hand this to someone else and have them shop and cook?

If the answer is no, you refine—change the format, revise the rules, or ask ChatGPT to optimize based on feedback.

In ORKA, every artifact is a mirror. It reflects the system behind it. And every edit you make improves that system—turning a one-off plan into a repeatable, flexible framework for next week.

This is ORKA in real life: one task at a time, elevated through systems thinking.

Practice, Progress, and Community

You don't need to master ORKA in theory before you start using it. You just need to start—one task, one project, one system at a time.

The real power of ORKA isn't just the framework itself—it's what happens when you practice it inside a supportive community.

That's why each Pod isn't just a course. It's an active group of AI-native professionals solving real-world challenges together. You'll:

• Get feedback on your real workflows and agents
• Watch others iterate in real-time and borrow what works
• Share templates, prompts, and artifacts that are field-tested

Small improvements stack fast. When you learn in the context of doing, ORKA becomes second nature. You go from surviving the shift to leading it.

What's Next: The Full ORKA Framework

You've just completed ORKA 101—a mindset shift from overwhelmed to orchestrated. You've seen how Orchestration, Rules, Knowledge Work, and Artifacts fit together to form a modern system for intelligent productivity.

But this is just the surface. If you found this helpful, the full ORKA Framework expands your capability across every major dimension of working with AI systems. Think of this as your map to becoming an AI-native leader in your field.

Here's the full curriculum:

ORKA Module Series (Recommended Progression)

ORKA 102 – Orchestration: Designing Flows, Not Just Tasks: Learn how to map, modularize, and own your workflows from strategy to delivery.

ORKA 103 – Rules: Building Precision Through Prompts: Create adaptive, reusable rules that dramatically improve output quality across tools.

ORKA 104 – Knowledge Work: How to Deconstruct Thinking for Delegation: Break apart cognitive tasks and apply agents to do the heavy lifting.

ORKA 105 – Artifacts: Turn Systems into Outputs That Compound: Capture, refine, and operationalize outputs that create strategic leverage.

ORKA 201 – Workflows vs. Agents: Understand the shift from rule-based automation to autonomous agents. Learn when to use which, and how to test both.

ORKA 202 – Agent Design Patterns: Architect reusable agent templates for research, strategy, content, and execution. Learn the anatomy of a good agent.

ORKA 203 – Tool Ecosystems: The best AI tools, wrappers, platforms, and orchestration layers for everyday work. Not hype—what works.

ORKA 204 – Memory + Model Strategy: Understand how tools store context, what to trust, and how to tune models for different tasks.

ORKA 205 – Enterprise vs Project Design: Learn to scale systems thinking across teams, departments, and deliverables. ORKA isn't just for solo use.

ORKA 206 – AI System Security: Design for data privacy, reproducibility, and audit trails when building operational AI systems.

ORKA 207 – Performance + Measurement: Create dashboards and measurement loops for system quality, ROI, and agent success.

Whether you're leading a team or launching solo, this framework helps you think bigger, build faster, and scale intelligently.

Ready to join Pod #15 and go deeper? Secure your spot or stay informed at Join the Pod and be part of the next wave of AI-native operators.