Claims QA Pack — verifying worksheets before any public ship

Affiliate disclosure: Some links are affiliate links. As an Amazon Associate I earn from qualifying purchases. See Disclosure.

Series: local-AI affiliate desk series (article 13)

In our local AI deployment workflow, moving from raw generation to public release requires a strict gatekeeping step. This is the thirteenth entry in the series and acts as a procedural checkpoint. After the human coordination hand-off described previously, this article defines the Claims Quality Assurance (QA) pass. The objective is simple: treat every claims worksheet as a draft needing exhaustive verification before shipment. Worksheets list assertions but do not verify them (claims worksheets list claims but do not verify them).

Why claims worksheets are not proof

A common error in automated workflows is assuming a completed worksheet equals evidence. It does not. A worksheet is merely a compilation of model-generated assertions; it lacks the ontological weight of primary evidence. Relying on these documents as proof creates an illusion of certainty that crumbles under scrutiny (empty-cite leftovers block publish until filled or removed). Leftover empty citations block publication until filled or removed.

The propagation risk is significant. If an automated system generates a claim based on a hallucinated statistic or outdated data, and that claim publishes without human intervention, the error amplifies to the entire audience. There is no immediate correction mechanism once content is live. Therefore, every worksheet must be treated as a draft requiring rigorous QA protocols, not a final product ready for distribution (ebook Ch6/9/10 v2-local empty-cite sweep status).

We also enforce a strict allow-list policy for internal routing. Suspiciously generated /go/ parameters often mask affiliate tracking scripts or lead to non-existent landing pages. Distinguish between legitimate, pre-approved URLs and invented redirects. The protocol is simple: reject any URL requiring multiple hops or displaying a generic gateway page without clear context. Valid links in this series are restricted strictly to /go/ollama, /go/hugo, /go/apple-silicon, /go/mlx-audio, and /go/github-pages. Any deviation from this allow-list is an immediate red flag requiring investigation before deployment (/go/ slugs must match links.csv allow-list).

Empty-cite sweep across ebook Ch6/9/10 leftovers

The QA pack requires an “empty-cite” sweep. This audit focuses on residues in Chapter 6, Chapter 9, and Chapter 10 of our documentation, where citation density is historically low or non-existent. The goal is to identify statements lacking a concrete source link or footnote, flagging them as potential failures (human primary-source or frontier check before ship).

In technical writing, every assertion must be anchored. Statements regarding market trends in Chapter 6 require verifiable references. If a claim suggests a specific trajectory in local model adoption without a source, it must be flagged for removal. Similarly, data points concerning user retention in Chapter 9 cannot stand alone; they require the original dataset or study attached as evidence [CITE:

The remediation strategy is binary: locate the original source to attach it securely, or remove the unsupported assertion entirely. We do not accept “plausible” explanations as substitutes for facts. If a claim appears in the draft but lacks a corresponding citation in the reference list, it is incomplete. This process ensures the final publication maintains intellectual integrity and avoids presenting speculation as established fact.

Allow-list /go/ vs invented redirects

The protocol dictates rejecting any URL structure requiring multiple hops or displaying a generic gateway page without clear context. The system operates on the principle of least surprise: a user clicking a link should arrive directly at the intended resource, not through intermediate pages obscuring the origin. Maintaining a strict allow-list of /go/ slugs ensures all internal routing remains transparent and secure.

This discipline extends beyond navigation. It involves a proactive stance against automated generation of suspicious parameters. If a model suggests a link to a resource outside our predefined list of trusted tools—such as an unverified cloud provider or a proprietary dashboard without public documentation—it must be discarded. Trust is built on consistency; deviating from the approved set of links undermines the reliability of the content ecosystem.

Human verification queue (frontier or primary sources)

The queue process mandates that high-stakes assertions route to the human review pipeline before any public shipment. No claim bypasses this stage, regardless of model confidence. Statements touching regulatory compliance, financial data, or scientific consensus require manual sign-off. This acts as a safety valve, preventing error propagation with real-world consequences.

If a primary source cannot be located within a defined timeframe, the fallback position is strict: withhold the claim rather than publish it unverified. Silence is preferable to misinformation. It is better for a section to remain incomplete than for the publication to carry an unverified assertion that could later be retracted. This approach ensures output remains defensible and aligned with accuracy standards.

Night

Conversely, during standard business hours outside this night window, operations shift strictly to inspect-only modes. No new content generates for public consumption without prior manual sign-off. This distinction is vital for compliance; it ensures deployments respect a strict temporal boundary preventing accidental live releases during low-visibility windows where human oversight might be reduced.

Compliance notes must mark deployment logs clearly. Any attempt to bypass the daytime inspect-only protocol or push content during the Night Adherence to these time-based restrictions ensures critical reviews happen when analysts are available, while automated systems handle off-hours tasks without risking public exposure.

Before you ship

This thirteenth article serves as a reminder that automation is a tool, not an arbiter of truth. Until every claim backs with primary evidence and every link passes the allow-list check, content remains in draft status. Only after passing this QA pass should material be considered ready for public shipment.

(Affiliate Disclosure: This article contains affiliate links to local-AI tools and infrastructure services such as Ollama, Hugo, Apple Silicon hardware, MLX Audio, and GitHub Pages. Purchases made through these links may result in a commission at no additional cost to you, supporting the maintenance of this series.)