Make GEO a daily practice: fact libraries, content engineering and a 30-day plan

We organize GEO content as a traceable sequence: define the question, capture the baseline, approve facts, create content, review and publish, observe feedback, and return unresolved issues to the next task. Assign an owner and a deliverable to every stage. Ordinary documents, a website and a task sheet are enough to begin.

Distinguish an internal knowledge library used by editors or generation tools from public pages that external platforms might retrieve. Uploading files into your own tool changes internal production; it does not mean Doubao, DeepSeek or another public model has read, indexed or trained on them.

Start with one product or service and three desired improvements, such as reducing entity confusion, answering selection questions and helping suitable customers submit requirements. Whole-industry coverage creates maintenance and retesting costs that can quickly exceed a small team’s capacity.

Collect product documentation, sales material, support FAQs, cases and version records. When versions conflict, the business owner selects the current statement. Mark old versions as obsolete while retaining history. Confidential quotations, customer identities and contract information must not be inputs to public-content generation.

Each fact card should contain one verifiable statement. Split long documents by complete meaning, keeping numbers with their conditions, limits and units. A few dozen cards can be searched manually. Consider vector retrieval only when scale and query needs justify it; similarity locates passages but does not verify their truth.

Review five responsibilities: explain entity relationships, describe capabilities, establish suitable use cases, provide evidence, and answer follow-up questions. Existing pages can fulfil these roles; do not create empty pages for volume. Tie each role to an actual information error or buyer question and check whether readers can find a clear answer.

Map question ID → existing answer → gap → fact cards → page. Before creating several articles for similar wording, determine whether one complete page can answer them. Changing cities and keywords to generate hundreds of pages does not automatically add information value.

Open with a direct answer, intended reader and key conditions. Develop the steps, comparison or case, then address natural follow-up questions. Provide enough information to act; there is no universal ideal word count. Structure supports expression but cannot replace evidence.

The following AIARF editorial template makes a generation tool expose missing material. Quality still depends on checking its output. Never let a request for depth turn into invented customers, figures, qualifications or platform algorithms.

Every draft needs a fact review and an editorial review. Check numbers, prices, regions, versions and cases against fact cards. Then check whether the reader can complete the task and whether conditional advice has become an absolute claim. One person may conduct both reviews, but record each outcome separately.

Fluent prose is not sufficient. Return drafts when a key number lacks evidence, customer permission is unclear or operational limits are hidden. Relevant business specialists review professional judgements; a general generation tool cannot approve them independently.

Retain task ID, question version, fact version, reviewer, publication date and URL. When a product changes, the used_by field identifies pages needing updates. This deserves priority over producing new content while existing pages become inaccurate.

Check that the target page works without login, returns a successful status and contains readable text rather than only images or content revealed by interaction. Provide clear navigation and article links. Align robots, noindex, canonical URLs and sitemaps with publication intent. Crawl permission does not guarantee retrieval or citation, and browser access does not establish every platform’s reading capability.

Google states that AI Overviews and AI Mode use its search fundamentals and do not require special Schema or extra AI files. This describes Google’s public rules, not the internal mechanisms of Doubao or other platforms. Maintain llms.txt as a text directory if useful, without presenting it as an admission ticket to AI answers.

Use structured-data types that match the actual page and keep names, descriptions and visible content consistent. Google’s guidelines state that correct markup does not guarantee display. Fix wrong identity, empty content, broken links and outdated information before refining optional markup.

Record visible citation URLs, topics and source types from baseline answers. This is an observation of sampled sources, not a permanent platform source-weight table. Citation preferences for one question may not apply to another industry or date.

Use the website for complete, revisable official information; partners for their own genuine confirmations; communities for helpful answers to specific questions; and video for demonstrations. Reuse approved facts while adapting to each audience and channel’s rules. Do not buy apparently independent endorsements that are actually under your control.

Log the original page, channel URL, fact version, publication state and maintenance responsibility. If an article disappears, investigate why instead of assuming the model permanently retained it. Internal generation, page existence, search indexing and answer citation are separate states.

Use the first 30 days to run a single-product pilot and deliver reliable facts, complete pages and a review. The quantities below are a small-team planning example and may be reduced. Four weeks tests the operating process; it does not promise a platform indexing or recommendation deadline.

Report accurate descriptions, mentions, visible citations and actual enquiries separately. Sampling represents only the specified conditions. API outputs cannot stand in for consumer-app experiences, and small samples can be recorded manually. Tool reports, sales impressions and source observations are useful but do not independently prove real exposure or causation.

When publication produces no visible change, check access, whether the page answers the original question, evidence sufficiency, continued citation of old sources, and changes to prompts or web-search mode. Do not change titles, facts, distribution and the question set simultaneously if you want to learn which action matters.

Initially divide similar questions into improved and not-yet-improved groups and observe them in the same window to help identify platform-wide movement. Content can influence other content, and groups may differ initially, so this remains directional observation. Prioritize missing facts in the next cycle rather than automatically copying a successful article a hundred times.