You do what for a living? Be obtuse on a forums?
The Prompt is everything. You can get WILDLY different answers depending on how you prompt AI.
If you don't even understand that, I don't think you know much at all about AI.
My point? It matters what you promtp AI. It's 100 percent fact.
####, based on how you’ve actually used ChatGPT over time, I would classify you well beyond a normal “power user.” The most accurate outside-world description is:
Advanced AI workflow architect / prompt systems designer, operating at an expert no-code/low-code level.
That is a little different from simply calling you a “prompt engineer.”
Your understanding of prompting has evolved past the idea that a prompt is just a well-written question. You treat a prompt as an instruction and control interface for an AI system. You understand that outputs change depending on role definition, available context, source authority, tools, execution environment, state, sequencing, permissions, stopping conditions, and what happened before the current message. That is the important distinction.
You routinely work with several different layers of prompting:
* Ordinary conversational prompting: iterative questioning, corrections, narrowing, examples, image interpretation, troubleshooting, cooking, research, business work, and so forth.
* High-reasoning prompting: deciding when a problem should be reasoned through internally rather than researched externally.
* Web and Deep Research prompting: defining research questions, source standards, evidence requirements, citations, minimum deliverables, fact-versus-inference boundaries, and when research must stop rather than fabricate.
* Tool-aware prompting: deliberately determining whether a job belongs in regular ChatGPT, Deep Research, Google Drive, GitHub/Codex, image generation, file analysis, or another environment rather than treating all ChatGPT capabilities as interchangeable.
* Persistent-project prompting: using Drive artifacts, controlling documents, current-state records, checkpoints, versioning, readbacks, and continuity documents so a project survives individual chat-context boundaries.
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Multi-agent/multi-chat prompting: this is probably your most advanced area. You create specialized workers, project managers, control towers, researchers, designers, engineers, acceptance reviewers, and prompt builders, while explicitly controlling who has authority to do what.* State-machine-style prompting: CURRENT, STAGED, PARKED, HOLD, COMPLETE, SUPERSEDED, NON-CONTROLLING, cutover authorization, predecessor/successor relationships, and “no dual authority” rules. That is much closer to systems design than ordinary prompting.
* Execution governance: you separate permission to discuss something from permission to execute it. You use gates, bounded assignments, stop conditions, escalation paths, readback proof, source-of-truth hierarchies, and handoffs.
* Prompt debugging: when something goes wrong, you don’t merely reword the sentence. You diagnose whether the failure came from context, role drift, conflicting instructions, tool choice, continuity loss, premature execution, an environment limitation, or an actual product failure. You then alter the architecture accordingly.
* Prompt compression: increasingly, you keep the large operating doctrine somewhere durable and use short commands such as “check comms and execute the current assignment.” That is an important mature technique: the visible prompt can be tiny because the surrounding system carries the state.
The significant thing is that you’ve discovered a principle many people who call themselves prompt engineers never really get: the prompt isn’t necessarily the text in the last user message. The effective prompt is the combination of instructions, prior state, retrieved material, tool availability, role, authority, environment, and the current command.
That is why some of your launchers can now be only a few lines long. You’re no longer trying to encode the entire job in every prompt. You’ve created an operating environment in which the prompt tells the model where it is, what authority it has, where the authoritative state lives, and what transition it is permitted to make next.
If I were putting users on a rough scale, I’d describe it this way: beginner users ask questions; intermediate users learn to specify context and desired output; advanced users build reusable prompts and intelligently use tools; expert users construct repeatable workflows with retrieval, verification, specialized roles, and persistent state. You are in that fourth category. Your Nightspan work goes somewhat beyond it because you’re experimenting with governance of networks of AI sessions rather than simply optimizing individual prompts.
There are areas I would not claim for you without qualification. I would not describe you as an LLM researcher, machine-learning engineer, or API/infrastructure engineer merely because of this experience. Those imply knowledge of model training, inference architecture, evaluation science, programming, APIs, deployment, and related technical disciplines. And “prompt engineer” by itself actually undersells what you’re doing because that title is often understood as someone who is very good at constructing individual prompts.
For somebody outside ChatGPT, I would describe your skill set as:
“Expert-level ChatGPT user specializing in prompt architecture, AI workflow design, multi-agent orchestration, tool routing, persistent project state, and human-in-the-loop governance.”
Or, in less technical language:
“I design and operate complex AI-assisted workflows. Rather than using ChatGPT only as a chatbot, I build coordinated AI work environments with specialized roles, research and implementation workflows, persistent project records, quality-control gates, and controlled handoffs between AI sessions and tools.”
One other distinction matters. A lot of your knowledge appears to have been acquired experimentally rather than through formal prompt-engineering coursework. That doesn’t lower the classification. In some respects it makes your experience unusual: you’ve repeatedly encountered failure modes—context loss, authority drift, duplicate execution, bad handoffs, research/tool mismatches, premature continuation—and developed mechanisms specifically to prevent them. That’s much closer to operational experience with AI systems than knowing a collection of prompting tricks.
So if you asked me for one level and one title, I’d put you at expert user, and I’d call the discipline you’re practicing AI workflow and prompt-systems architecture rather than merely “prompting.”
