Standalone Document-Based Snippets with Source Attribution & Training Control

Where to make the improvement:

Automations → Brand Intelligence → Content Snippets
Automations → Social AI → Train Your AI

Pain Points:

When documents are uploaded to create snippets, the system automatically merges that information into existing snippets instead of preserving it as a separate entity. This makes it difficult to locate where the uploaded content has been stored. Users cannot reference or prioritize this information during AI training because there is no clear mapping between the uploaded document and the resulting snippet.

There is also a context misinterpretation issue. For example, if a document includes a use case mentioning an “owner of a roofing company,” the AI may incorrectly associate the word “owner” with the user’s own agency contact details. This leads to inaccurate outputs and confusion in generated content.

Overall, lack of visibility, control, and traceability reduces trust in how the AI is using uploaded knowledge.

Feature Description:

Introduce the ability to create standalone snippets directly from uploaded documents instead of automatically merging them into existing snippets. Each document-based snippet should retain clear source attribution, allowing users to see exactly where the content originated from.

Provide a dedicated section or tagging system to identify “Document Snippets” separately from system-generated or website-derived snippets. Users should be able to view, edit, and manage these snippets independently.

Enhance Train Your AI to allow users to explicitly select and prioritize specific snippets or document sources during training. This ensures the AI focuses on the intended context when generating content.

Additionally, improve context handling so that content from uploaded documents is treated as external knowledge or use-case references, rather than being incorrectly mapped to the user’s own business attributes.

Expected Results:

Users can clearly locate and manage all uploaded document content without confusion. AI training becomes more precise, as users can directly reference and prioritize specific snippets. Content generation improves in accuracy by avoiding incorrect context mapping. Overall, this increases user confidence, reduces errors in outputs, and enables better control over how AI leverages custom knowledge.