Google’s 2025 search now rewards content that is deeply helpful, structured, and machine-readable, while LLMs favor simple, self-contained answers, together reshaping SEO into a combination of technical precision and AI-aware writing techniques. For sustained visibility, strategies must align with Google’s June 2025 core update and the rise of AI Overviews/AI Mode, while also optimizing for chunked retrieval used by large language models.
What changed in 2025?
Google’s June 2025 core update came with quality signals, emphasizing experience, expertise, authoritativeness, and trust, with a long multi-week rollout and notable volatility across YMYL and commerce queries. AI features like AI Overviews and AI Mode expand zero-click journeys, demanding content that can be excerpted accurately and stands alone contextually. Sites must assume fewer clicks and design content to satisfy intent on the SERP and within AI-generated summaries.
Core SEO technologies
- Structured data at scale:Implement JSON-LD for Article, FAQ, HowTo, Product, Organization, LocalBusiness, and more to improve understanding and eligibility for rich results and AI features; validate via Rich Results Test and Search Console.
- AI features readiness: Follow Google’s guidance to make content compatible with AI Overviews/AI Mode clear answers, high-quality sources, and safe, people-first content aligned to E-E-A-T.
- Passage and chunk optimization: Write in small, self-contained sections under question-led H2/H3s so LLMs can cite discrete chunks without losing meaning.
- Topical authority models: Build comprehensive topic clusters with internal links, statistics, expert quotes, and original research to become a default citation source for AI answers.
- Technical health: Ensure fast CWV, clean semantic HTML, robust sitemaps, and impeccable crawlability to maximize discovery and indexing stability across updates.
- Entity and knowledge graph optimization: Use the Organization, Person, and sameAs schema, consistent NAP, and authoritative profiles to strengthen entity understanding used by both Google and AI systems.
- Safety and attribution signals: Transparent authorship, bylines, citations, and outbound links to primary sources improve trust for both core ranking systems and AI features.
LLM-first writing techniques
Use question answer structuring to guide readers and machines alike: make each subheading a natural, user-phrased question, then open with a crisp 1–2 sentence answer before adding brief context so the point is clear even in isolation. This style respects intent upfront, reduces ambiguity, and helps both skimmers and AI systems capture the essence without misreading nuance.
Follow extractive-friendly formatting to make every line carry weight: keep one idea per paragraph, prefer short sentences, and add bullets for steps or key takeaways, with clean, definitional statements that can be quoted directly without extra stitching by an assistant or search snippet. This improves readability for people while making machine extraction accurate, faithful, and context-safe in summaries or answers.
Ground writing in facts and figures so the content stands tall: include concrete statistics, clear definitions, enumerated steps, and explicit constraints or caveats; these specifics act like anchors that language models and search features can verify and reuse, reducing uncertainty and boosting trust signals. Numbers, process points, and source-backed statements also help users decide faster, which is ultimately the goal of helpful content online.
Close with FAQs and How-Tos that match on-page schema for better eligibility: Add a compact FAQ section with direct answers and a stepwise how-to for common tasks, then mirror them with structured data so snippet systems and AI answers can recognize scope, sequence, and relevance cleanly. This small but disciplined addition often improves coverage in rich results and increases the chance of accurate AI citations, especially for practical, search-heavy topics in India.
Technical SEO stack to adopt now
- Schema orchestration: Automate JSON-LD injection for key templates; monitor errors with Rich Results Test and schema validators to keep data clean at scale.
- Semantic HTML and sectioning: Use <article>, <section>, <header>, <aside>, and ARIA roles so crawlers and AI features can map content boundaries more reliably.
- CWV performance: Optimize LCP, INP, and CLS through image compression, priority hints, lazy loading, and CSS/JS reduction to maintain rankings through volatility.
- Indexing control: Maintain XML sitemaps, proper canonicalization, hreflang where needed, and strict robots/meta directives to ensure the right pages are indexed and surfaced.
- Logging and monitoring: Track AI Overviews appearances, zero-click impacts, passage-based impressions, and FAQ/HowTo rich result coverage to iterate content systematically.
Strategy for AI Overviews and AI Mode
- Meet intent in first lines:Begin with a definitive statement that directly answers the core question to improve the odds of being extracted into AI experiences.
- Source credibility: Provide author bios, methods, timestamps, and links to primary research; AI features prefer high-trust sources with clear provenance.
- Anti-hallucination support: Use explicit definitions, constraints, and warnings to reduce misinterpretation; this increases the likelihood of accurate summarization by AI.
- Maintain freshness: Update high-traffic pages with dated sections and changelogs; AI systems weigh recency for time-sensitive topics.
What to slow down
- Thin “SEO text” without substance: Pages built solely for keywords without expertise signals tend to lose ground post-update and are overlooked by AI features.
- Overlong blocks: Rambling paragraphs underperform in chunk retrieval; keep passages short and self-contained.
- Schema without parity: Markup that does not match visible content risks rich result ineligibility and trust issues in AI extraction.
Editorial and governance practices
- People-first checklists: Align with Google’s self-assessment on helpful content; ensure each page demonstrates real expertise and adds value beyond summaries.
- Fact-checking and sourcing: Cite sources inline and maintain reference sections, enabling both Google systems and LLMs to verify claims rapidly.
- Consistent updates: Refresh pillar content quarterly; document changes to signal maintenance and reliability to crawlers and AI features.
FAQs
1. What are the biggest SEO shifts in 2025?
Google’s June 2025 core update and the expansion of AI Overviews/AI Mode require content that is structured, verifiable, and immediately answers user intent in extractable chunks.
2. How do LLMs “choose” which pages to cite?
LLMs prefer pages with QA-structured headings, short self-contained passages, clear facts, and strong trust signals such as authorship and citations.
3. Does structured data directly improve rankings?
Structured data is not a direct ranking factor but improves understanding and eligibility for rich results, indirectly aiding visibility and click-throughs.
4. How should content be formatted for AI answers?
Start with a direct answer, keep one idea per paragraph, add bullets for steps or facts, and use FAQ/HowTo schema for machine-readable sections.
5. What technical priorities protect rankings through updates?
Strong CWV, clean semantic HTML, accurate indexing controls, and continuous monitoring of AI and rich result appearance are essential foundations.