“The process is more important than the prompts. Prompts are ephemeral — they age, get filtered, or lose effectiveness as models evolve. Process, however, is adaptive.” — Lou
Core Idea
Prompts are fragile. They’re tied to specific model versions, specific contexts, specific moments in time. A prompt that produced brilliant newsletter content last month might produce mediocre content today — because the model updated, or the audience shifted, or you’re working in a slightly different context. Every time you need output, you start from a semi-blank slate. You’re carrying the bucket.
The alternative is a three-level architecture where the work of designing prompts happens once at a higher level of abstraction:
Level 1 — The Task Agent. The prompt that does the work: writes the article, answers the client question, generates the proposal. This is where most people currently operate. It is the most fragile level.
Level 2 — The Domain Orchestrator (Meta Prompt). A prompt that doesn’t do the task but generates the task prompt dynamically. It has domain expertise and knows when to call which task agents. Crucially, it’s context-aware — it pulls relevant information into the task prompt before executing.
Level 3 — The System Architect (Meta-Meta Prompt). A prompt that is domain-agnostic. It doesn’t know about article writing or coaching. It knows the structure of a perfect prompt system. It interviews the user, understands what they want to accomplish, and generates the Level 2 orchestrator for that domain.
When you operate at Level 1, you maintain each prompt manually. When the model updates or your audience changes, you rewrite the prompt. When you operate at Level 3, you have a system that regenerates itself based on current reality.
What Makes Levels 2–3 Powerful: Context Engineering
The underlying mechanism is not clever instructions — it’s the deliberate construction of the information environment in which a prompt operates. Not “add context to your prompt,” but design a system that dynamically assembles the right context before the task executes.
In practice: run a simulated expert debate on the topic, have a judge evaluate it, summarize the conclusions — all before asking for the final article. By the time the article request arrives, the context contains a structured, multi-perspective analysis. The article addresses all relevant angles, pre-empts objections, and takes a defensible position. Ten minutes of context engineering produces content that would have taken hours of research and drafting manually.
The bucket-to-pipeline reframe: carrying a bucket serves the immediate need but scales terribly. The pipeline changes the question from “how do I carry this water?” to “what system ensures the water is always available?” You invest once in building the system; thereafter, the system does the work.
Practical Application
Building Your First Meta Prompt:
Start with a task you do repeatedly with AI (write a newsletter, create a client summary, draft a proposal).
- Identify your task-level prompt. What prompt do you currently use for this task?
- Generalize it. Ask the AI: “How could I make this prompt dynamic — adapting to any version of this type of task rather than this one instance?”
- Templatize. Ask the AI to write a template version usable for any [article / proposal / coaching response] without changing the core instructions.
- Build the context layer. Ask the AI: “What information would this prompt need in its context to produce the best output? List the data sources, questions it should answer first, and knowledge it should activate before executing.”
- Combine. Merge the template and context layer into a single meta prompt. Test and iterate.
The 10-Minute Article Process:
- State the topic and thesis you want to argue
- Ask the AI to simulate a 10-minute expert debate from two opposing positions
- Ask the AI to analyze the debate and declare a winner with reasoning
- Ask the AI to write a 1,500-word piece for [your specific audience] based on the debate analysis, taking the winning position
This sequence produces an article grounded in the strongest arguments on both sides. It can’t be gamed by AI-typical generality because you’ve forced intellectual tension before writing.
Reflection prompts:
- What tasks am I performing repeatedly with AI where I’m “carrying the bucket” — doing the same work each time because I haven’t built the pipeline?
- What is my coaching process, stripped to its essential steps? Could an AI follow it if encoded as a meta prompt?
- What would it mean for my business if the AI consistently embodied my methodology rather than just responding to individual questions?