FPF Reference

    Decide Whether FPF Fits

    Preface node heading:decide-whether-fpf-fits:409

    What this page is

    This is generated FPF reference text from the specification preface or supporting sections. It helps interpret FPF; it is not FPF Reference product documentation.

    Methodology

    Use it to understand how the specification wants to be read, then return to a route, pattern, or work packet for active work. Cite generated IDs only when the wording changes the task decision.

    Content

    Use FPF when ordinary discussion is no longer enough to keep work coherent. Typical signs:

    • several teams, experts, tools, or AI agents must reason about the same work;
    • the real-world test is slow, expensive, noisy, risky, or politically hard to repeat;
    • different readers need different reports, dashboards, explanations, or decisions about the same underlying work;
    • names, roles, responsibilities, options, evidence, or quality criteria are starting to blur;
    • the team needs a current view of possible approaches, not just one recommendation;
    • a decision is small enough to make now but important enough to leave a durable reason.

    FPF is probably too heavy when the task is small, feedback is fast and cheap, the vocabulary is already stable, the decision will not be reused or audited, and a quick answer is enough.

    FPF is mainly useful for people who have to keep difficult work understandable across boundaries:

    • engineers and systems engineers working with complex products or operations;
    • researchers building claims that others must inspect or reuse;
    • platform and AI teams coordinating humans, models, tools, and approvals;
    • safety, assurance, compliance, and regulatory leads who need visible evidence and responsibility boundaries;
    • managers and product leaders who must compare options, budgets, risks, and delivery promises without hiding trade-offs.

    There are three common ways to use FPF:

    1. Human-only: use it as a writing and review discipline for meetings, notes, decisions, and technical documents.
    2. Mixed team: use it to keep specialists, managers, safety leads, and AI assistants aligned around the same work.
    3. AI-assisted: attach or index the specification, ask for plain-language project help first, and use pattern names only when they make the answer easier to check.

    Stronger AI does not remove the need for FPF. AI can generate fluent options quickly, but projects still need to decide what counts as evidence, which option is being compared, who may rely on an answer, when a claim is stale, what remains only a guess, and what work is actually authorized. FPF helps make those boundaries explicit before a confident answer becomes an expensive mistake.

    Core ideas in plain language:

    • local teams may use local meanings, but translation must be explicit when work crosses a boundary;
    • the thing itself, its description, a dashboard about it, a decision about it, and the work done to change it are not the same;
    • keep several options alive until the comparison is clear enough to choose;
    • say what "better" means before optimizing or scoring;
    • make trust depend on evidence, freshness, scope, and intended use;
    • publish different views for different readers without changing the underlying claim;
    • use mathematics or formal models when they clarify what structure is preserved, what is lost, and what can be checked.

    Last Updated: 2026-06-08 — upstream FPF commit 093d30e8 (github.com/ailev/FPF)