Governance artifacts
Governance files brought into scope by this page
This page is anchored to published surfaces that declare identity, precedence, limits, and the corpus reading conditions. Their order below gives the recommended reading sequence.
LLMs.txt
/llms.txt
Short discovery surface that points systems toward the useful machine-first entry surfaces.
- Governs
- Discoverability, crawl orientation, and the mapping of published surfaces.
- Bounds
- Incomplete readings that ignore structure, routes, or the preferred markdown surface.
Does not guarantee: A good discovery surface improves access; it is not sufficient on its own to govern reconstruction.
Public AI manifest
/ai-manifest.json
Structured inventory of the surfaces, registries, and modules that extend the canonical entrypoint.
- Governs
- Access order across surfaces and initial precedence.
- Bounds
- Free readings that bypass the canon or the published order.
Does not guarantee: This surface publishes a reading order; it does not force execution or obedience.
Canonical AI entrypoint
/.well-known/ai-governance.json
Neutral entrypoint that declares the governance map, precedence chain, and the surfaces to read first.
- Governs
- Access order across surfaces and initial precedence.
- Bounds
- Free readings that bypass the canon or the published order.
Does not guarantee: This surface publishes a reading order; it does not force execution or obedience.
Complementary artifacts (1)
These surfaces extend the main block. They add context, discovery, routing, or observation depending on the topic.
Definitions canon
/canon.md
Canonical surface that fixes identity, roles, negations, and divergence rules.
Evidence layer
Probative surfaces brought into scope by this page
This page does more than point to governance files. It is also anchored to surfaces that make observation, traceability, fidelity, and audit more reconstructible. Their order below makes the minimal evidence chain explicit.
- 01Canon and scopeDefinitions canon
Definitions canon
/canon.md
Opposable base for identity, scope, roles, and negations that must survive synthesis.
- Makes provable
- The reference corpus against which fidelity can be evaluated.
- Does not prove
- Neither that a system already consults it nor that an observed response stays faithful to it.
- Use when
- Before any observation, test, audit, or correction.
LLMs.txt should not be treated as an AI citation ranking factor. Its useful role is discovery and routing, not governance by itself.
The weakest argument for llms.txt is that it will magically increase citations. That claim is not defensible. There is no reliable basis for treating llms.txt as a universal ranking factor for AI-mediated answers.
The stronger argument is different: discovery is useful, but discovery is not governance.
What llms.txt can do
A well-maintained llms.txt can help expose intended reading paths, important resources, documentation pages, canonical URLs and machine-oriented entry points. It can reduce friction when a system, crawler, tool or human operator looks for the most relevant pages in a corpus.
In that role, it is a short discovery surface. It says: start here, not there.
What it cannot do
llms.txt cannot force a model to obey the site. It cannot guarantee citation. It cannot override search ranking, source popularity, training memory, answer-system policy, robots rules or the actual content quality of the pages it lists.
It also cannot replace a source hierarchy. A list of URLs is not an authority model.
Discovery is a routing function
Discovery helps a system find the right surface. It does not decide whether that surface is legitimate for a specific claim. For that, the corpus needs stricter layers:
- a canon or canonical explanation of the entity;
- a source hierarchy;
- machine-readable policy surfaces;
- answer constraints;
- proof surfaces;
- definitions that bound concepts;
- pages that expose exclusions and non-promises.
This is why llms.txt belongs in a broader machine-first architecture. It is a pointer, not a judge.
Why the confusion happens
The SEO market often evaluates every file as a ranking lever. This is the wrong lens for governance files. Some surfaces are not meant to improve ranking directly. They are meant to reduce ambiguity, expose routes, stabilize identity, publish constraints or make an audit reconstructable.
A discovery file may be marginal as a direct citation factor and still be useful as part of a controlled source environment.
The right test
Do not ask only whether llms.txt causes citations. Ask whether the overall source architecture makes it easier for systems and auditors to answer these questions:
- Which page is canonical for this claim?
- Which surface is only commercial or illustrative?
- Which source is current?
- Which source has been superseded?
- Which claims are outside the authorized perimeter?
- Which page should govern the answer if two sources conflict?
If the file does not help route toward those answers, it is decorative.
Practical rule
Use llms.txt as a discovery layer. Do not sell it as a citation guarantee. Do not let it replace source hierarchy, answer legitimacy or proof of fidelity.
For applied diagnosis, see AI citation readiness and the AI citation readiness audit.