1. Observations that are hard to reproduce elsewhere
Summarizing official documentation rarely exceeds the value of the source. Publish implementation conditions, failures, before-and-after measurements, and decision rationale so the page becomes primary evidence.
Google’s 2026 AI optimization guide likewise emphasizes non-commodity content with unique viewpoints and experience. WEPLET treats experiment conditions and revision history as default editorial metadata.
2–3. Direct answers and explicit scope
Answer the title’s question near the opening. Do not hide behind ‘it depends’; name the conditions that would change the conclusion in the same answer block.
Let each section solve one sub-question and make the argument visible through headings. Use tables and lists for comparison, but do not optimize for artificial ‘chunking’ at the expense of coherent writing.
- Question → short answer → conditions → evidence → action
- Attach period, sample, and tool to every measurement
- Separate definitions from editorial judgment
4–5. Nearby evidence and accountable authorship
Do more than append a long source list: explain the basis next to claims likely to change. Do not give official standards, direct measurements, and third-party analysis identical evidentiary weight.
Keep brand name, site description, and authorship consistent across visible copy, metadata, and structured data. Transparent editorial and review processes are better than invented experts or inflated credentials.
6. Prove freshness with changes, not dates alone
Changing a date is not freshness. Record what was rechecked and which conclusion changed. Durable principles can retain their original publication date when the substance is still valid.
Review standards, browser, and search-feature articles against official change logs. Update internal experiments when new samples accumulate, while preserving earlier results rather than overwriting them.
7. A discoverable technical foundation
Even excellent writing struggles when crawlers cannot access it or its canonical URL shifts. Include core copy in server output and keep status codes, internal links, canonicals, and sitemaps consistent.
llms.txt can be a convenient map for people and some tools, but it is not a requirement for Google’s AI features. Do not present it as a replacement for crawlable content and foundational SEO.
Pre-release checklist
- ✓ Does the opening directly answer the title with conditions?
- ✓ Does the page contain experience, measurement, or judgment that is hard to copy?
- ✓ Are primary sources close to claims that may change?
- ✓ Are authorship, brand, and update dates consistent?
- ✓ Is core content accessible without login or client execution?
Frequently asked questions
Will llms.txt increase AI citations?
No shared standard or ranking signal guarantees that outcome. It can be a useful site map, but crawlability, originality, and clear evidence come first.
Does adding many FAQs help?
Only when they answer genuine follow-up questions. Google discontinued FAQ rich results in 2026, so mass-producing FAQs for search decoration has little basis.