Prepare useful knowledge for AI

Start with the questions the material should answer

List a few real questions before collecting files. You may need to compare options or understand a difficult document. A colleague may need the current process. A customer may need to know how a service works. Each question calls for relevant evidence and an explanation that fits its reader.

Use those questions to choose the material. A small set of relevant, understandable sources is easier to review than an undifferentiated archive. Keep documents that establish the answer, explain its context, or identify a meaningful exception.

• Name the people who will use the knowledge.
• List their recurring questions.
• Identify which sources are needed to answer those questions.

Choose sources with a clear owner and purpose

Check who owns each source, what it covers, and whether it is current. An approved policy, a draft proposal, and an old help article should not appear equally authoritative. Preserve useful context such as an effective date, document title, or the original web address.

Review what was imported from files and web pages. Confirm that important text, tables, and qualifications are readable. For a snippet, include enough surrounding context to preserve its meaning and identify where it came from. A summary should remain distinguishable from the original source.

• Identify the person or team responsible for the source.
• Check the version, effective date, and relevant scope.
• Resolve conflicting instructions or label the unresolved difference.
• Exclude information that the intended readers should not access.

Organize the Library around a useful purpose

Help.ai Library holds reusable knowledge for work in AI Chat and AI Agents. Group material by a purpose people can recognize, such as product support, an internal process, or research for a project. Select the relevant Library context for the task rather than assuming every stored document will be useful.

For example, a service-onboarding collection might contain the current onboarding checklist, a description of each service option, and answers to recurring preparation questions. An old launch proposal belongs elsewhere if it describes plans that were never adopted.

Use titles that distinguish the sources at a glance. “Customer onboarding — approved process — September 2026” tells a reader more than “Final notes v3.” Keep ownership and review information in the source or your normal document process; a descriptive title does not replace checking the content.

Review answers to real questions

Ask representative questions and compare the answers with the underlying material. Include a routine question, an exception, and something the sources do not establish. Check whether important conditions survive the summary and whether uncertainty remains visible.

For example, ask “What does a new customer need before their first appointment?” Then ask about an option with different requirements, and a scenario the material does not cover. If the answer overgeneralizes, fix the unclear source or narrow the instructions before relying on it.

Adding a document to the Library does not establish that every answer is true or current. Source selection, retrieval, and interpretation can all leave gaps. Review consequential answers against the material and ask for clarification when the evidence is incomplete.

Make updates part of the source owner’s work

Agree who will review the knowledge when a product, policy, or process changes. Update the authoritative source, review the imported material, and revisit the questions affected by the change. A collection should not keep offering an old instruction simply because nobody owns its maintenance.

Keep access appropriate to the task and audience. Material used for internal research may contain detail that should not be part of a customer-facing agent’s context. Recheck the selected knowledge and instructions when an agent’s role changes.