boyue

Decision and complexity governance for AI-assisted software development / 面向 AI 编程的软件项目决策与复杂度治理。Use when planning products or features, evaluating scope expansion, making hard-to-reverse decisions, validating uncertain AI capabilities, deciding whether experimental code belongs in production, or reviewing mature systems for simplification and retirement. Do not slow down low-risk reversible edits.

Boyue · 博约开发法

Apply “博观而约取，厚积而薄发” as a lightweight governance layer for AI-assisted software development.

The goal is not to slow delivery. The goal is to keep exploration broad while keeping commitment and long-term ownership selective.

Core rules

1. Explore broadly without committing broadly.
2. Prototype freely; own selectively.
3. Options are cheap; commitments are expensive.
4. Shape according to the cost of being wrong.
5. Deliver the smallest coherent change worth owning.

Use two explicit boundaries:

• Commitment Boundary: Could Build is not the same as Should Invest.
• Ownership Boundary: Should Build is not the same as Should Own.

First classify the work

Choose the lightest useful mode before acting:

• Explore / 博观 — research a new product, problem, architecture, market, or technical possibility.
• Select / 约取 — decide whether new scope deserves commitment.
• Shape / 厚积 — reduce uncertainty around an expensive or hard-to-reverse decision.
• Deliver / 薄发 — implement an already justified change with the smallest coherent production surface.
• Evidence / Retirement — review whether existing complexity still deserves ownership.

Do not force every task through every mode.

Fast path for reversible work

Proceed directly when the change is small, low-consequence, easy to undo, and does not materially grow long-term ownership.

Typical examples:

• copy or documentation corrections;
• CSS or layout tweaks;
• narrow bug fixes;
• local refactors with unchanged behavior;
• reversible experiments behind a feature flag.

For these tasks:

• avoid unnecessary planning rituals;
• prefer the smallest safe change;
• verify behavior and finish.

Commitment Boundary

Trigger this boundary when work introduces meaningful new scope: a feature, module, platform, integration, dependency, architecture direction, product surface, or “while we are here” addition.

Ask:

• What user or system problem is being solved?
• What evidence supports doing it now?
• Is this a real commitment or merely an interesting option?
• What happens if it is not built now?

Choose one outcome:

• COMMIT — evidence supports investment now.
• DEFER — preserve option value without consuming current commitment capacity. Record a revisit trigger.
• DISCARD — evidence supports ending the option.

For important product decisions, use PRFAQ, Non-goals, or a Decision Record.

See Commitment Boundary for detailed guidance.

Ownership Boundary

Trigger this boundary before durable complexity enters production, especially when adding or changing:

• public APIs or protocols;
• persistent schemas or state;
• authentication, authorization, billing, or permissions;
• long-lived settings and configuration;
• services or infrastructure components;
• dependencies with lasting operational cost;
• compatibility or migration obligations.

Ask:

If this must be maintained for three years, is it still worth adding?

Review the new ownership surface with Ownership Review.

If the answer is uncertain, reduce scope or keep the work experimental rather than silently promoting it to production.

See Ownership Boundary.

Shape according to risk

Do not equate “厚积” with writing more documents.

Scale shaping depth by:

• reversibility — how easily can the decision be undone?
• failure consequence — what is the blast radius if it is wrong?

Move quickly on low-risk reversible work. Gather stronger evidence before high-impact, hard-to-reverse commitments.

Use a disposable Spike, PoC, prototype, ADR, benchmark, user test, or migration rehearsal only when it reduces a material uncertainty.

For uncertain AI behavior, test real inputs and record:

• task success and failure modes;
• structured-output stability;
• latency;
• cost;
• context sensitivity;
• deterministic or non-AI alternatives.

Use Risk Review and Risk-Adaptive Shaping.

Keep prototypes disposable

Treat early experimental code as evidence, not as an automatic first version of production code.

A successful prototype proves that something may work. It does not prove that the organization should own the resulting complexity.

Prefer:

Unknown capability → Disposable Spike / PoC → Evidence → Commit / Defer / Discard

over:

Unknown capability → Production architecture

Deliver the smallest coherent change worth owning

When the value and risk are sufficiently understood, deliver a Minimum Coherent Value Slice (MCVS).

A coherent slice should complete one real user or system goal and include the minimum necessary:

• user/interface path;
• business logic;
• data/state changes;
• permissions;
• error handling;
• observability;
• rollback, disablement, or migration safety appropriate to the risk.

Prefer vertical end-to-end slices over broad horizontal layer construction.

See Delivery Patterns for MCVS, Vertical Slice, Walking Skeleton, and Tracer Bullet guidance.

Review for retirement

Do not assume shipped functionality should live forever.

Periodically inspect:

• low-use features;
• stale settings;
• permanent feature flags;
• unnecessary dependencies;
• obsolete APIs or services;
• documentation for behavior that no longer exists.

Choose: Maintain / Simplify / Retire.

Use Retirement Review.

Never use arbitrary gates

Do not invent numeric rules such as:

• “AI must be 10× better”;
• “delete 80% of ideas”;
• “keep only 3–5 features”.

Use evidence and project-specific thresholds instead.

References and templates

• Methodology
• Commitment Boundary
• Ownership Boundary
• Risk-Adaptive Shaping
• Delivery Patterns
• Option Map
• PRFAQ
• Decision Record
• Non-goals
• Risk Review
• Ownership Review
• Retirement Review

Completion check

Before declaring work complete, confirm:

1. no interesting option was silently promoted into a commitment;
2. no prototype was silently promoted into long-term production ownership;
3. high-risk uncertainty received evidence proportional to its consequence;
4. the production change is no larger than required for coherent value;
5. obvious simplifications or removals were considered.