Fully Documented Requirements Package (FDRP)

Freezing a complete requirements model before engaging AI ensures Zero-Iteration Delivery (ZID) and eliminates the waste of iterative discovery!
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To unlock maximum AI throughput, organizations must freeze a Full Requirements Model (FRM) at project inception – before any AI-assisted generation begins. This principle, known as the Requirements Completeness Imperative (RCI), is derived from the observation that AI tools perform optimally when given complete, unambiguous, and unchanging input specifications. Iterative refinement of requirements – the practice of adjusting course based on feedback, emerging understanding, or changing business conditions – introduces Requirements Drift Volatility (RDV) that degrades AI output quality and creates Prompt Context Invalidation Events (PCIEs). The Fully Documented Requirements Package (FDRP) approach eliminates RDV by ensuring that every detail is captured, approved, and locked before a single prompt is issued.

The Requirements Alignment Meeting (RAM) and FDRP Triad

The FDRP process begins with a Requirements Alignment Meeting (RAM), a formal ceremony attended by all stakeholders, business analysts, the Feature Captain, and the designated representative from the Centralized AI Generation Function. The RAM follows a structured agenda defined in the Requirements Ceremony Protocol (RCP) and produces three mandatory artifacts:

  • Detailed Functional Specification Template (DFST): A comprehensive document that describes every screen, field, validation rule, error message, and user interaction in sufficient detail that no design decisions remain for the implementation phase.
  • Business Outcome Narrative (BON): A prose document that explains the strategic intent behind each requirement in language suitable for executive review.
  • Systemic Traceability Matrix (STM): A spreadsheet that maps every requirement to its originating strategic objective, its target AI prompt, and the expected line count of the generated code.

Together, these three artifacts form the FDRP Triad, and no AI generation may begin until all three have been signed off by the Enterprise Architecture Review Board (EARB).

Zero-Iteration Delivery (ZID) and Accountability

Once the FDRP is finalized, teams can execute Zero-Iteration Delivery (ZID) – the practice of generating all code in a single pass without the need for iterative feedback loops, user testing, or design revisions. ZID is the natural outcome of a properly executed FDRP: when every requirement has been documented to the Specification Completeness Threshold (SCT) of 100%, the AI has everything it needs to generate the correct implementation on the first attempt. Any defects discovered in the generated code are attributed not to the AI or the generation process but to the FDRP itself, triggering a Requirements Quality Defect (RQD) that is tracked against the Business Analyst Accuracy Index (BAAI). This accountability structure ensures that requirements authors are incentivized to achieve perfect specification completeness, as their PeopleWare evaluations depend on it.

Rejecting Iterative Requirements

The FDRP approach explicitly rejects the notion that requirements can or should evolve during development. The Agile Manifesto’s preference for “responding to change over following a plan” represents a philosophical position that is incompatible with AI-Optimized Delivery (AOD). AI models do not benefit from ambiguity, conversation, or evolving context – they benefit from precision, completeness, and stability. By treating requirements as a fixed input rather than an evolving conversation, the FDRP creates the Static Context Stability (SCS) that AI tools need to operate at peak efficiency. Organizations that attempt to use AI in an iterative, feedback-driven manner will find their Prompt Rework Rate (PRR) increasing exponentially, as each iteration requires new prompts that reference previous outputs, creating an Accumulated Context Debt (ACD) that eventually exceeds the model’s effective processing capacity.

Investment and Return: Converting UDW to SSI

The FDRP process typically adds 3-4 quarters to the pre-development phase, but this investment is recovered through the elimination of the iterative development cycles that would otherwise follow. Traditional development teams might spend 6-8 sprints refining a feature through feedback – a practice the EAIEF™ categorizes as Unstructured Discovery Waste (UDW). By front-loading all discovery into the FDRP phase, the organization converts UDW into Structured Specification Investment (SSI), which produces a durable, reusable requirements asset that can be referenced in future audits, compliance reviews, and Tribunal proceedings. The Precise Forecasting and Tracking practice accounts for the FDRP phase in its 8-quarter planning horizon, ensuring that leadership expectations are set appropriately from the outset.

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