Build an Evidence Library Before You Build an AI Content Engine

The article emphasizes the importance of establishing an evidence library before developing an AI content engine for marketing purposes. A marketing team initially asked an AI assistant to write a service page, but the organization provided content without an agreed-upon evidence boundary. This led to the assistant using three internal documents with varying prices, an outdated presentation with a retired credential, and a customer result whose permission for public use was unclear.

The resulting paragraph may have been well-written, but the failure occurred before any writing took place, as the organization did not provide content with a clearly defined evidence boundary.

The author, the founder and CEO of INSIDEA, an Elite HubSpot Partner, argues that building a marketing evidence library before expanding an AI content program is crucial. This involves maintaining a relationship between claims, sources, owners, permitted uses, and review dates. The article suggests that a document is often too broad a unit for approval, as one page may contain current service descriptions, outdated statistics, and customer quotes approved for private proposals but not public articles.

By creating a claim record for statements that matter, one can include the exact approved wording or supported fact, its supporting source, owner, permitted audience, effective date, review date, and necessary qualifications. Distinctions should be made between direct quotations, paraphrases, and measured results. The article also highlights the need to separate truth, permission, and freshness when dealing with claims.

Verified evidence does not imply publication permission, and publication permission does not guarantee current accuracy. An expired review date should trigger reassessment rather than automatic deletion or rejection of related statements.

The author provides a hypothetical customer example, keeping the measured result, measurement method, relevant period, permission record, and approved anonymity level together. If public permission is absent, a general method or an explicitly synthetic scenario should be used instead. The article stresses the importance of preserving the origin and transformation of claims, giving conflicting facts a review route, separating internal context from public evidence, and defining access and output rules separately.

HubSpot's AI-settings guide describes controls for AI access and data sources, but these settings do not replace claim-level publication decisions. The article concludes by emphasizing the significance of using an approved information-handling process to label public, internal, customer-restricted, and other relevant categories, ensuring that retrieval returns evidence, not just answers.