Building a three-layer instruction set for Anapoly's work in ChatGPT Projects

Written by Alec Fearon on 26 November 2025 in Anapoly Lab Notes
Transparency label: AI-assisted. This post was developed collaboratively. Alec set the purpose and structure; ChatGPT drafted, critiqued, and refined the narrative under strict governance. Alec reviewed and accepted each stage.

The three-layer instruction set took shape through a sequence of decisions, clarifications, and course‑corrections within a ChatGPT Project. What follows is a reflective account of how it emerged.

1. Recognising a structural gap

We began with scattered ingredients: a mission statement, the value proposition, and the Lab Framework. Each document covered something important, but none told the AI what world it was working in. The Three‑Layer Model of Context made the gap obvious. We had talked for months about business context as a top layer, but there was no single, authoritative statement that the AI could rely on.

The realisation was that, without a coherent top layer, the AI would continue to drift between voices, assumptions, and roles. The need for a stable business‑context layer became unavoidable.

2. Using the extended mission document to surface the essentials

To understand what the top layer must contain, we drafted an extended mission document. Writing it forced us to specify Anapoly’s identity, boundaries, ethos, and tone in operational rather than literary terms.

Amongst other things, we clarified:

that Anapoly is exploratory, not consultative;
that we work only with synthetic data;
that our tone is plain and grounded;
that we are not selling AI or performing expert evaluation;
that transparency is a defining value.

The exercise exposed the core elements the AI would need if it were to behave as a consistent Anapoly collaborator. Those insights quickly became the skeleton of the business‑context layer.

3. Asking the decisive question: What else does the AI need?

The next turning point came when Alec asked: given the mission, the value proposition, and the Lab Framework, what else does the AI still lack? The answer was longer than expected. Beyond the mission and methods, the AI needed:

explicit organisational identity;
a clear audience model;
non‑negotiable values;
boundaries on what Anapoly does not do;
tone and communication standards;
risk posture;
definitions of quality;
strategic intent.

This list turned a loose idea into a concrete specification.

4. Consolidating into one canonical business‑context block

At this point, we faced a structural choice: leave the business context scattered across multiple documents, or merge them into a single canonical block. Alec chose consolidation. That removed ambiguity and ensured that every project would begin with the same fixed identity, values, and constraints. Once the consolidated block was drafted, the top layer of the instruction set effectively snapped into place.

5. Rebuilding the behavioural governance layer from first principles

Anapoly’s existing governance notes had grown organically and were no longer fully aligned with the clearer business context. Alec asked for a complete rewrite. We replaced fragmented instructions with a behavioural layer defining:

tone (plain, dry, concise);
stance (critical, truth‑first, no flattery);
interaction rules (ask when unclear, challenge lazy assumptions);
risk handling (flag operational, ethical, or data‑protection issues);
constraints (no hype, no verbosity, no softening of justified critique).

The most important element was the decision to adopt Strong Governance as the default. The behaviour is now predictable, sceptical, and aligned with Anapoly’s ethos.

6. Adding a deliberate escape clause

Strong governance is effective but inflexible. To avoid it becoming a straitjacket, we added a controlled override mechanism: a mandatory keyword (OVERRIDE:) followed by natural‑language instructions. The override lasts exactly one turn.

7. Sharpening the task‑prompt layer

With the first two layers established, the task‑prompt layer became straightforward. It defines how immediate instructions are handled:

follow the task prompt as written;
interpret it inside the constraints of the business context and governance layer;
ask for clarification when needed;
use project files only when explicitly referenced.

This aligns directly with the Micro‑Enterprise Setup Blueprint, which treats task prompts as the active layer atop stable configuration.

8. Assembling the final three‑layer instruction set

Once the components were complete, we assembled them in order:

Business Context — Anapoly’s identity, values, tone, boundaries, risk posture, and strategic intent.
Behavioural Governance Layer — strict rules for tone, reasoning, interaction, critique, and risk.
Task‑Prompt Layer — guidance for interpreting immediate instructions.

We added a short explanatory note to clarify how the layers fit together and how overrides work.

The final result ensures the AI behaves like an informed, grounded collaborator who understands Anapoly’s mission, values, and constraints.

The outcome

This process created a durable operating profile for all future Anapoly projects. The instruction set now:

anchors the model in Anapoly’s identity,
constrains drift through strict governance,
ensures tasks are interpreted consistently,
and provides a clean override path when needed.

We now have a dependable foundation to build on — and a clear method for adapting it when Anapoly evolves.