Build a Working Environment
Stop treating every prompt as a new conversation. Organize the problem, the source material, the project instructions, the working threads, and the artifacts so the AI can participate in work that continues over time.
Live · Hands-On · AI Productivity · Working Method
Structural AI for the Workplace
The productivity gain does not come from "using AI."
It comes from changing how the work itself is done.
It comes from changing how the work itself is done.
Structural AI teaches the actual working method used to research, reason, write, design, build, test, and develop substantial work with AI as a solo operator. It is not a prompt-engineering class and it is not a tour of AI products.
You will learn how to build a persistent working environment around the problem, maintain useful context, work from source material, separate workstreams, challenge the AI, verify the result, and progressively turn the work into finished artifacts.
The Drift Stack™
Identity → Frame → Boundary → Drift → Correction
The method was developed in real work
What Can One Person Actually Produce With AI?
Samirac's body of work was not produced by a large research department, writing staff, product team, web team, and development organization. Much of it was produced by one person learning how to work with AI as part of an integrated working environment.
The point of the course is not that AI did the work. The point is that the working method changed what one person could reasonably research, understand, build, test, document, and finish.
- 150+ articles and papers
- two books
- the collapse-order and theory-of-drift work
- Samirac's public websites and commercial pages
- dAIsy
- MindMesh
- AI RADAR™
- architecture, production code, technical documentation, and product development
The working method
What You Will Learn to Do Differently
Preserve Context Without Losing Control
Know what belongs in project context, what belongs in a source file, what should stay in a working conversation, and what should be written back into a durable artifact when a decision has been made.
Interrogate Instead of Accepting
Challenge answers, test reasoning, compare alternatives, force the problem into concrete examples, and learn to recognize when confident output is structurally wrong.
Separate Workstreams
Research, architecture, writing, coding, product decisions, source material, and execution do not have to collapse into one endless chat. Learn how to keep work connected without making it incoherent.
Develop Artifacts Iteratively
Use AI to move from rough problem to durable document, page, code, analysis, design, or decision. The objective is finished work that can survive outside the conversation.
Verify Before You Trust
AI can accelerate work and still be wrong. Learn where verification belongs, how to keep authority human where required, and how to avoid building a workflow around unverified output.
Bad habits become a ceiling
Using AI the Wrong Way Teaches You the Wrong Workflow
If you spend months treating AI like a faster search box, accepting the first answer, and starting every piece of work from an empty prompt, that workflow becomes normal. Structural AI teaches the working structure before those habits become the limit on what you can do.
- Opening a blank chat for every question and throwing away useful context
- Treating AI as an answer machine instead of a working partner inside a larger process
- Copying the first plausible output instead of interrogating the reasoning
- Letting one giant conversation become the project, the source of truth, the notes, and the final artifact at the same time
- Asking AI to automate a bad process before understanding the result the work is supposed to produce
- Confusing speed of output with quality, understanding, or completed work
Course environment
Why This Course Uses ChatGPT Projects
The underlying Structural AI method is larger than one model or vendor. Could the method be adapted to Claude, Grok, or another capable environment? Yes. But that is not how this course is taught.
We use ChatGPT because its project-based working structure is the environment in which this method was developed and repeatedly used in real work. Projects, project instructions, source files, continuing conversations, and persistent working context cleanly support the method being taught.
The course is not going to spend its time translating the workflow across several different products. We are going to use the environment we know supports the method cleanly so the class can focus on learning the method itself.
Participants need their own ChatGPT account. A basic account is sufficient for the course.
Live instruction
This Is Not a Prerecorded Prompt Course
Public training is delivered live in five instructor-led Zoom sessions. We work through projects, source material, questions, reasoning, artifacts, and workflow decisions in the environment itself rather than talking about productivity from slides.
Private company training can use the organization's actual workflows and operating problems so the method is applied to work people already perform.
Choose the format
Course Options
Public Live Training
Structural AI for the Workplace
$750
Per participant
- • Five live instructor-led Zoom sessions
- • Hands-on work inside ChatGPT Projects
- • Prepared examples and guided exercises
- • Enrollment is recorded through your Samirac account
Private Company Training
Bring Your Actual Workflows
$12,500
Starting price · up to 10 participants
- • Use your real workflows, source material, and operating problems
- • Apply the method directly to the work your team performs
- • Flexible private delivery
- • Company scope is confirmed before payment and scheduling
The objective
Learn how to work with AI at a completely different level of productivity.
The goal is not to teach you a list of prompts. It is to teach you how to structure the working relationship between you, the problem, the context, the source material, the AI, the verification process, and the finished work.