Contextual Scaffolding

a framework

Creating a valuable knowledge-based product with AI depends on two things: the AI must clearly understand what’s required, and it must stay aligned with that requirement as the work progresses. Our solution is contextual scaffolding — a structured yet lightweight method that keeps the AI grounded and relevant from start to finish.

A key feature of this approach is that the AI collaborates in building the scaffolding itself, starting with the initial programme of work. This ensures the AI is involved from the outset, shaping the context it will work within and adapting it as the project evolves.

Contextual scaffolding works through two complementary structures. The phase model shapes the entire lifecycle, from initial concept to operational use. The project model organises collaborative work within each phase, ensuring the AI works with current priorities, materials, and deliverables.

Both models are deliberately flexible, applied only as firmly as the situation demands, so that structure supports progress without stifling it.

Phase Model (Cradle-to-Grave Structure)

The phase model provides end-to-end structure across the full lifecycle of the product.

Concept — Explore potential benefits, clarify purpose, and draft a business case.
Definition — Specify the intended final deliverable in sufficient detail to support structured development.
Development — Develop and test the final product.
Acceptance — Validate the deliverable in the hands of its intended users, ensure it meets defined needs, and bring it into use.
Operation and Maintenance — Use, monitor, and refine the product until it is retired.

Not every phase will require a full project structure, but where structured collaboration is needed, the project model applies.

Project Model (Within-Phase Scaffolding)

Within each phase, a project-based approach provides structure for collaborative delivery.

1. Defined Goal

The project is established to deliver a clear, knowledge-based product. Typically, this will be the output required from a phase.

2. Project-Based Structure

Work within the phase is organised into discrete stages, each with defined objectives and outputs.

3. Stage Gates

Progression from one stage to the next requires a quality review of outputs. Only satisfactory products allow forward movement.

4. Product-Focused

Each stage produces one or more interim products. Most are “technical” in nature and build towards the final deliverable; some are to manage progress.

5. Human-AI Collaboration

The AI acts as a collaborator.

The human is the decision-maker.
The AI adapts to the nature of work at hand.
Because contextual scaffolding keeps pace with the progress of work, the AI remains grounded and its responses stay relevant and precise.

6. Adapted PRINCE2 Principles

Draws selectively from PRINCE2, including:

Product-based planning
Management by exception (e.g. gates)
Clear roles and responsibilities
Embedded quality control

7. Quality Management

A simplified quality management process ensures control:

Each product has a defined description or acceptance criteria
Quality is assessed at stage gates
Feedback loops enable iteration and correction

Example: Preparing a Competitive Tender

When preparing a competitive tender, the required knowledge product is a complete, high-quality bid submission. However, at the outset, the scope, fulfillment strategy, resource requirements, and pricing model will not be clear.

Phase Model in Action
The work progresses through phases – starting with a concept phase to assess feasibility and value, followed by definition (specifying scope and win themes), development (producing the actual bid), acceptance (review and sign-off), and finally operation and maintenance (post-submission follow-up or revision).

Project Model in Each Phase
Within each phase, a lightweight project structure is applied to guide collaboration with the AI. For example, in the definition phase, the AI helps analyse requirements, map obligations, and develop outline responses. In the development phase, it helps draft, refine, and format bid content.

Contextual Scaffolding
At every stage, contextual scaffolding ensures the AI is working with the right background, priorities, and current materials. Thus it can focus on what matters most, and contribute in ways that are coherent, precise, and aligned with both the tender requirements and internal strategy.

Transparency label justification. This document was developed through structured collaboration between Alec Fearon and ChatGPT. Alec provided the core ideas, framing, and sequence. ChatGPT contributed to the organisation, refinement, and drafting of the text, under Alec’s direction. The content reflects a co-developed understanding, with human oversight and final decisions throughout.

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