FAQ
Clear answers to practical questions.
Explore direct, useful answers to common questions about brand clarity, websites, WordPress, AI workflows, creative strategy and knowledge systems. Each article is designed to help you understand the issue, make a better decision and know what to do next.
Search by question and sort by recency or title.
How do I keep an AI knowledge pack current and relevant?
Keep an AI knowledge pack current by adding review dates, version notes, source freshness checks, archive rules and a clear process for replacing outdated material. A knowledge pack should evolve by governed updates, not by constant uncontrolled dumping of new files.
How do I prevent an AI knowledge base from becoming a junk drawer?
Prevent an AI knowledge base from becoming a junk drawer by setting intake rules, source labels, exclusion criteria, review dates, topic boundaries and cleanup loops. Do not save everything. A useful knowledge pack contains selected, structured and maintained material, not every note the team ever collected.
How do I turn many articles into a structured knowledge system?
Turn many articles into a structured knowledge system by preserving sources, extracting core ideas, grouping related concepts, separating evidence from interpretation, removing duplicates, creating usage rules and adding review loops. The goal is not a bigger folder; it is a system that can be searched, updated and reused.
How do I use AI agents without losing control of the knowledge system?
Use AI agents with bounded permissions, clear source rules, approval gates, logs, rollback paths and defined tasks. Do not let agents freely rewrite, delete or merge knowledge without review. The system should make agent work inspectable and reversible.
How do you package a knowledge pack for Custom GPT, Skill files and private AI workflows?
Package a knowledge pack for AI use by separating the buyer-facing knowledge file, starter prompts, Custom GPT instructions, Custom GPT knowledge, Skill file, usage boundaries and optional advanced AI setup handoff. Each file should have a clear role, exact name, upload map and boundary note.
How do you verify a completed knowledge pack before release?
Verify a completed knowledge pack by inventorying the uploaded files, confirming exact filenames, checking internal references, reviewing onboarding and usage boundaries, testing prompts, checking Custom GPT or Skill setup, removing unsupported claims, and confirming the package matches the manifest before release.
How should product pages describe knowledge packs without overclaiming?
Describe a knowledge pack as a structured AI-ready reference with prompts, workflows, learning assets, boundaries and private AI workflow context. Avoid claims about guaranteed accuracy, live updates, professional advice, support, refunds, resale rights, public-bot use or deployed RAG unless the package explicitly grants them.
What information should not go into an AI knowledge pack?
Do not put private credentials, client-confidential material, sensitive personal data, unsupported claims, stale drafts, duplicated clutter, copyrighted material without rights, or material that conflicts with the pack’s purpose into an AI knowledge pack. Exclusion rules are as important as collection rules.
What NotebookLM derivative reports should you generate before building a knowledge pack?
Before building a knowledge pack, generate derivative reports that expose the source set from different angles: per-source extraction cards, evidence ledger, claims table, workflow maps, contradictions and boundaries, retrieval tests, concept maps, playbooks, prompt patterns and a final NotebookLM bridge export.
What should a customer knowledge-pack ZIP include?
A customer knowledge-pack ZIP should use one clear top-level folder and separate buyer-facing files into Knowledge Pack, Concept Learning Assets, AI Companion Setup, Start Here Readme and Playbook, and Usage Boundaries. It should exclude raw sources, internal logs, process prompts and operating artefacts.
When should you run pre-NotebookLM source preparation?
Run pre-NotebookLM source preparation when the source set is scattered, duplicated, stale, oversized, poorly named, or context-heavy. The goal is to create a source-preserving export and, where useful, a clearly labelled AI research expansion before NotebookLM starts generating derivative reports.
FAQ categories
Browse by answer hub
These category hubs group related FAQ articles into focused answer pages while the main FAQ index keeps search, category, and intent filters available.
RGD Profile / Accounts
Answers about RGD profile setup, account routes, identity, and how the public profile system fits together.
Clearer Brands
Questions about brand clarity, positioning, voice, trust, and making a business easier to understand.
Websites That Work
Practical answers on website structure, pages, content, conversion paths, and making a site easier to use.
WordPress Reality
Clear answers about WordPress care plans, plugin risk, maintenance, performance, ownership, and practical tradeoffs.
SEO Answer Hub
Search-focused answers about indexing, sitemap hygiene, Google visibility, structured content, and useful pages.
Knowledge Pack Process
Answers about building, packaging, importing, maintaining, and improving structured knowledge packs.
AI With Judgement
Practical answers about using AI without outsourcing judgement, strategy, customer value, or responsibility.
These entries are short, structured answers designed to match how people search for practical guidance.
Each response starts with a direct answer, then adds examples, implementation steps, and practical caveats.
Use the filters to browse by question intent and search for the exact phrasing you care about.
How these answers are written
Each page starts with a direct answer, then a practical section you can reuse immediately.
Entries are short, source-aware, and include explicit caveats where the answer is conditional.
The goal is not completeness for completeness’s sake, but usable clarity for the decision in front of you.