Primary entity targeting
• Select one core entity that exactly matches the blog’s main topic (e.g., Electric Vehicle Charging Stations) and make it the focus of the H1, first 100 words, meta title, meta description, URL slug, image alt-text, and one structured data schema type.
• Add granular schema markup (Article, FAQPage, HowTo, Product, or Event) enriched with the primary entity’s @id, sameAs links to Wikipedia/Wikidata, and relevant properties (brand, location, price, date, steps).
Secondary and related entities
• From Google’s Knowledge Graph, People Also Ask, and competitor top-ranking pages, list 8-12 supporting entities (e.g., Level 2 Charger, kWh, CCS Connector, Tesla Supercharger, Federal Tax Credit, Range Anxiety).
• Use each as subheadings (H2/H3) or early-paragraph anchor text to create clear topical clusters.
• Interlink each secondary entity to an in-depth internal page if available; otherwise include a short definition and authoritative external link (.gov, .edu, OEM site).
LSI and semantic keyword map
• Build a keyword graph with term frequency analysis and embedding tools (e.g., Google NLP API, GPT embeddings) to surface 25-40 high-co-occurrence phrases.
• Classify into intent buckets: informational (what is a Level 2 charger), navigational (nearest EV charger map), commercial (EV charger installation cost), transactional (buy Level 2 EVSE).
• Sprinkle LSI phrases naturally inside headers, lists, FAQs, image captions, and anchor text; keep keyword density <2 % per variant to avoid stuffing.
• Include long-tail question variants exactly as seen in PAA and Reddit threads to score featured snippet and AI summary eligibility.
NLP optimization
• Write in a conversational, entity-dense style: short sentences, active voice, 0.6–0.8 Flesch reading ease.
• Use semantic triples (subject-predicate-object) to make relationships explicit: EV owners install home chargers; DC fast chargers reduce wait time.
• Add quantitative facts, tables, and step-by-step lists so language models extract structured answers.
Prompt-ready sectioning for AI search and ChatGPT
• Insert an FAQ section with 6–8 schema-marked Question/Answer pairs (max 40 words per answer, answer first, no fluff).
• Provide a TL;DR summary block (90–120 words) after introduction; label it TL;DR for easy capture by AI snippets.
• Insert a key takeaways list at the end, each bullet ≤15 words.
Content freshness and topical authority
• Update statistics, regulations, incentives every 90 days; add a Last Updated date in schema and visible text.
• Publish 2–3 supporting articles per month and link back using exact-match anchor of secondary entities to build topical authority network.
Technical implementation
• Use semantic HTML5 (article, section, header, nav, aside).
• Place entities in JSON-LD schema and in visible text to reinforce machine readability.
• Ensure core web vitals and accessibility (ARIA labels, alt text with entities).
• Generate automated XML/RSS update pings to Bing, Google, and AI index APIs.
E-E-A-T signals
• Add author bio with credentials and sameAs to LinkedIn, IEEE, or relevant professional body.
• Cite at least 5 reputable sources with inline links and publication dates.
• Include reviewer schema if content is fact-checked by a licensed electrician or engineer.
Measurement and iteration
• Track entity recognition via Google NLP API and InLinks; if primary entity salience score <0.2, increase mention density and proximity to verbs.
• Monitor SERP features for target questions; adjust FAQ phrasing to match winning snippets.
• Re-run embedding similarity every quarter to discover emerging co-occurrence terms and refresh LSI list.