--- title: "Content Freshness & AI Search: Why Recency Drives Citations (2026)" description: "Content freshness is a ranking and citation signal in AI search. 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Both are ISO 8601 strings inside JSON-LD and are read by both Google and LLMs. lastReviewed is a separate property — primarily defined on MedicalEntity — but the convention of surfacing 'last reviewed' in JSON-LD or in visible UX is now widespread." }, { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "Visible Freshness UX Patterns: 'Updated April 2026' Badges", "url": "https://alicelabs.ai/en/insights/content-freshness-ai-search#visible-freshness-ux-patterns", "description": "Visible freshness signals are the user-facing counterpart to Schema.org dates. The most common pattern is an 'Updated [Month Year]' badge near the title, sometimes paired with 'Originally published' and 'Last reviewed' lines. 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Google publicly documented freshness as a ranking signal with the 2011 Freshness Algorithm Update, and it remains in effect for queries Google interprets as time-sensitive. AI search engines — ChatGPT Search, Perplexity, Google AI Overviews — also prioritise recent sources for time-sensitive queries. Freshness is not the only signal, but for many queries it is a qualifying threshold rather than a tie-breaker." } }, { "@type": "Question", "name": "What is the difference between datePublished and dateModified?", "acceptedAnswer": { "@type": "Answer", "text": "datePublished is the date an article was first published — set once, never changed. dateModified is the date of the most recent meaningful update. Both are Schema.org Article properties and should be ISO 8601 strings (YYYY-MM-DD) inside JSON-LD. Update dateModified when content changes substantively; do not bump it for typo fixes or boilerplate edits." } }, { "@type": "Question", "name": "How often should I update my content?", "acceptedAnswer": { "@type": "Answer", "text": "It depends on content type. News content needs review every 7-14 days. Pricing pages every 30 days. Regulatory and compliance content every 30-60 days plus event triggers. Evergreen deepdives and pillar pages every 90 days — the Alice Labs default. Glossary and definitional content can stretch to 180 days. The cycle should be documented; surprise refreshes are less effective than disciplined cadence." } }, { "@type": "Question", "name": "Will updating dateModified without changing content help my rankings?", "acceptedAnswer": { "@type": "Answer", "text": "No, and it can hurt. Search engines and LLMs detect 'date stuffing' — bumping dateModified without meaningful edits. 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Outside medical content, it has no formal Article-level analogue, so most editorial sites surface 'last reviewed' as a visible UX line plus dateModified in JSON-LD. The convention is widely understood by humans and LLMs alike." } }, { "@type": "Question", "name": "Does adding 'Updated April 2026' to my title help?", "acceptedAnswer": { "@type": "Answer", "text": "It can — for time-sensitive queries. Putting the year in the title page or H1 ('… in 2026', 'Updated April 2026') is a long-standing SEO pattern, and AI search engines pick up the same cue. The risk is staleness: when April 2026 ends, the title becomes a liability. Pair the in-title date with a real review cadence so you remember to update it." } }, { "@type": "Question", "name": "What is the Alice Labs review cadence?", "acceptedAnswer": { "@type": "Answer", "text": "Alice Labs runs a 90-day default review cycle on evergreen content with tier overrides for high-decay types (pricing, regulatory, statistics roundups) and event triggers for legal or competitive changes. Every article carries a visible Published / Last reviewed / Next review block, mirrored in Schema.org datePublished and dateModified. We verify outcomes through a quarterly LLMO Citation Benchmark." } } ] }, { "@context": "https://schema.org", "@type": "Dataset", "name": "Content Freshness & AI Search: Why Recency Is a Citation Signal", "description": "Content freshness is a ranking and citation signal in AI search. 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[Home](/en) [Insights](/en/insights) [AI Search & LLMO](/en/insights/ai-search) Content Freshness & AI Search: Why Recency Is a Citation Signal AI Search & LLMO Deep Dive Recent Last reviewed: 15 May 2026 · 102d ago # Content Freshness & AI Search: Why Recency Is a Citation Signal ## TL;DR Quick Answer Cited by AI > Content freshness is the recency and active maintenance of a page, signalled through Schema.org datePublished and dateModified, visible 'Updated' badges, and a documented review cadence. AI search engines (ChatGPT Search, Perplexity, Google AI Overviews) prioritise fresh sources for time-sensitive queries, making freshness a direct citation signal. AI search engines treat recency as a quality signal. This deepdive explains the schema fields, UX patterns, and review cadences that keep your content in the citation set. Content freshness in AI search is the practice of signalling recency, accuracy, and active maintenance of web content through structured-data dates (datePublished, dateModified), visible UX badges, and a documented review cadence. It is a quality signal used by both traditional search engines and Large Language Model (LLM) retrieval systems when selecting which sources to trust and cite. ![Linus Ingemarsson - Author at Alice Labs](/images/linus-ingemarsson.png) Written by [Linus Ingemarsson ](https://www.linkedin.com/in/linus-ingemarsson/) ![Eric Lundberg - Reviewer at Alice Labs](/images/eric-lundberg.png) Reviewed by [Eric Lundberg ](https://www.linkedin.com/in/eric-lundberg-3530451bb/) Published May 6, 2026 · Updated May 15, 2026 11 min read Up to 40% Visibility lift from citation-rich content in generative engines [Aggarwal et al. 2024 (GEO paper)](https://arxiv.org/abs/2311.09735) 90 days Default Alice Labs review cadence for evergreen content Alice Labs methodology, 100+ Nordic implementations datePublished + dateModified The two Schema.org Article fields LLMs read for recency [schema.org/Article (verified spec)](https://schema.org/Article) What you'll learn(6 points) - Why freshness matters more in AI search than it ever did in traditional SEO - The exact Schema.org fields LLMs and search engines read for recency - Visible UX patterns ('Updated April 2026') that improve perceived trust - How to set a review cadence by content type — and when 90 days is too slow - Qualitative observations on how LLMs treat stale vs fresh sources - The Alice Labs review-cadence methodology used across 100+ Nordic implementations ## Key Takeaways - Freshness has been a publicly documented Google ranking signal since the 2011 Freshness Algorithm Update. - AI search engines (ChatGPT Search, Perplexity, Google AI Overviews) all consider recency when selecting sources for time-sensitive queries. - Schema.org Article defines two date fields LLMs read: datePublished (first publication) and dateModified (most recent meaningful update). - Visible UX freshness signals ('Updated April 2026') matter for human trust and for LLMs that read rendered HTML. - A 90-day review cadence is a sensible default for evergreen content; news, regulatory, and pricing pages need faster cycles. - Aggarwal et al. (2024) found citation-rich content lifted generative-engine visibility by up to 40% — fresh citations compound that lift. ### Contents 11 min left - [01 Why Freshness Matters More in AI Search Than Traditional SEO ](#why-freshness-matters-more-in-ai-search) - [02 Schema.org Freshness Signals: datePublished, dateModified, lastReviewed ](#schema-org-freshness-signals) - [03 Visible Freshness UX Patterns: 'Updated April 2026' Badges ](#visible-freshness-ux-patterns) - [04 Review Cycle Methodology: 90 Days Default, Faster Where Needed ](#review-cycle-methodology) - [05 LLM Citation Behaviour on Stale vs Fresh Content (Qualitative) ](#llm-citation-behaviour-stale-vs-fresh) - [06 The Alice Labs Review Cadence: A Proprietary Methodology ](#alice-labs-review-cadence) Part of [AI Search Optimization Guide](/en/insights/ai-search-optimization-guide) 01 / 06 Chapter ## Why Freshness Matters More in AI Search Than Traditional SEO In traditional SEO, freshness is one ranking factor among hundreds. In AI search, freshness is closer to a gating factor: LLMs increasingly retrieve in real time, and a model deciding which two or three sources to cite for a time-sensitive query will heavily prefer recent ones. Stale content is filtered out before the citation slot is awarded. Google has treated freshness as a ranking signal since at least the 2011 "Freshness Algorithm Update". Newer is not always better, but for time-sensitive queries it usually wins. AI search behaves the same way — only more so. ChatGPT Search, Perplexity, and Google AI Overviews all retrieve sources at query time, and only the top few make it into the visible answer. That citation slot is small. A query like "current EU AI Act timeline" will surface two or three sources, not twenty. Recency is one of the cheapest filters the model can apply first. **Three reasons freshness weighs heavier in AI search.** - **Cited answers are sparse.** Generative engines pick a handful of sources per answer. Stale pages are pruned early. - **Models are explicit about dates.** Many LLM answers now include "as of \[date\]" framing, which makes outdated sources visibly disqualifying. - **Trust compounds.** A page that is regularly updated signals an active maintainer, which is itself an E-E-A-T signal that traditional and AI search both reward. The practical consequence: in AI search, freshness is not a tie-breaker. It is part of the qualifying round. Recency is not the only quality signal Fresh content still has to be accurate, well-sourced, and entity-clear. A daily-updated page with weak citations will still lose to a quarterly-updated page with strong ones. Freshness amplifies quality — it does not replace it. 02 / 06 Chapter ## Schema.org Freshness Signals: datePublished, dateModified, lastReviewed In short Schema.org Article defines datePublished (first publication date) and dateModified (most recent meaningful update). Both are ISO 8601 strings inside JSON-LD and are read by both Google and LLMs. lastReviewed is a separate property — primarily defined on MedicalEntity — but the convention of surfacing 'last reviewed' in JSON-LD or in visible UX is now widespread. Schema.org Article is the canonical schema for editorial content. Two of its date properties matter for freshness: datePublished and dateModified. **datePublished.** The date the article was first published. Set once. Do not change it when you make minor updates — that destroys the historical signal. **dateModified.** The date of the most recent meaningful update. This is the field freshness depends on. Update it when content changes substantively, not for typo fixes. **Minimum example.** ``` { "@context": "https://schema.org", "@type": "Article", "headline": "Content Freshness & AI Search", "datePublished": "2026-05-06", "dateModified": "2026-05-06", "author": { "@type": "Person", "name": "Linus Ingemarsson" } } ``` **lastReviewed.** Schema.org defines this property on MedicalEntity, but the underlying idea — "we looked at this on date X and confirmed it is still correct" — is broadly useful. Many sites now surface a "Last reviewed" line in the rendered HTML even when JSON-LD does not include it. **What LLMs actually read.** Generative engines parse both the JSON-LD block and the visible HTML. If your dateModified in JSON-LD says May 2026 but the rendered byline says "Posted 2022", the model will hedge — and may pick the older signal. Reconcile both. **Three rules to follow.** 1. Use ISO 8601 (`YYYY-MM-DD`) in JSON-LD — not human strings like "May 6, 2026". 2. Update dateModified only on meaningful edits. Do not bump it daily for a stale page; LLMs and Google detect the pattern. 3. Make the visible date in the rendered HTML match the JSON-LD dateModified. Mismatch destroys trust. Do not fake-bump dateModified Programmatically updating dateModified without changing content is a known anti-pattern. Search engines and LLMs detect 'date stuffing' and discount the freshness signal — sometimes the entire page. 03 / 06 Chapter ## Visible Freshness UX Patterns: 'Updated April 2026' Badges In short Visible freshness signals are the user-facing counterpart to Schema.org dates. The most common pattern is an 'Updated \[Month Year\]' badge near the title, sometimes paired with 'Originally published' and 'Last reviewed' lines. They serve two audiences: humans (trust) and LLMs (which parse rendered HTML alongside structured data). Schema is invisible. Most readers — and many AI crawlers — also evaluate the page they actually see. Visible freshness signals close that gap. **Common patterns.** - **Updated badge.** "Updated April 2026" near the title or under the byline. Short, scannable, persistent. - **Three-line metadata.** "Published \[date\] · Last reviewed \[date\] · Next review \[date\]". Used on regulatory, medical, and high-stakes editorial content. - **Inline 'as of' framing.** "As of May 2026, the EU AI Act schedule is …" — this is the same pattern LLMs use, and mirroring it improves model alignment. - **Changelog block.** A collapsible "What changed in this update" list. Especially useful for guides and playbooks. **Where to put the signal.** Above the fold, near the byline, is the most extractable location. Putting it only in the footer means the model may not read far enough to find it. **Match the schema.** If JSON-LD dateModified says 2026-05-06 and the visible badge says "Updated April 2026", you have a 30-day mismatch. Pick one source of truth and render from it. **The Alice Labs convention.** Every article carries a three-date metadata block — Published, Last reviewed, Next review due — visible above the fold and mirrored in JSON-LD. The "next review" date is a public commitment that the page is on a cadence, not abandoned. Surface the next-review date A visible 'Next review due \[date\]' line is a forward-looking signal. It tells humans (and LLMs) that this is a maintained page, not a one-shot publish. It is one of the cheapest E-E-A-T upgrades you can make. 04 / 06 Chapter ## Review Cycle Methodology: 90 Days Default, Faster Where Needed In short A review cycle is a documented schedule for re-examining published content. A 90-day cycle is a sensible default for evergreen guides. News, pricing, and regulatory content need shorter cycles (7-30 days). Glossary and definitional content can stretch to 180 days. The cycle is the discipline; the dateModified is the proof. Reviewing content every 90 days is common practice across SEO and editorial teams. It is short enough to catch drift, long enough to be sustainable. But 90 days is a default, not a universal rule. Different content types decay at different rates. **Choosing the cycle by content type.** - **News / current-events posts.** 7-14 days. After that, re-frame as "\[Date\] retrospective" or unpublish. - **Pricing / commercial pages.** 30 days. These pages are often consulted by AI agents for comparisons; outdated prices are a trust break. - **Regulatory / compliance content.** 30-60 days, plus an event-trigger (review immediately on legal change). - **Evergreen guides and deepdives.** 90 days. The Alice Labs default for the LLMO content stack. - **Glossary / definitional pages.** 180 days. Definitions move slowly; reviewing too often is wasted effort. - **Statistics roundups.** 90 days, plus an immediate refresh whenever a referenced source publishes new data. **What "review" actually means.** A review is not a re-read. It is a structured check. 1. Are all cited statistics still current? Replace where superseded. 2. Are all external links still resolving? Fix or remove. 3. Has the underlying landscape changed? Edit the relevant section. 4. Update dateModified and the visible "Updated" badge — but only if step 1-3 produced a real change. Discipline beats heroics A documented 90-day cadence consistently beats sporadic 'big refreshes' once a year. AI search rewards the page with the most-recent meaningful dateModified, not the page with the longest one-time rewrite. Recommended review cadence by content type — Alice Labs methodology Content type Recommended cadence Trigger for early review Decay risk News / current-events 7-14 days Any related event Very high Pricing / commercial 30 days Pricing change High Regulatory / compliance 30-60 days Legal or rule change High Statistics roundups 90 days New source data Medium Evergreen deepdives 90 days Major industry shift Medium Pillar / topic hubs 90 days New cluster article Medium Comparison / vs pages 60-90 days Competitor product change Medium-high Glossary / definitional 180 days Term redefined publicly Low Source: Alice Labs methodology, applied across 100+ Nordic enterprise implementations (2024-2026) ![Linus Ingemarsson](/images/linus-ingemarsson.png)![Eric Lundberg](/images/eric-lundberg.png)![Alice Holmgren](/images/alice-holmgren.png) Alice Labs practitioner team ## Want a freshness audit on your top pages? Alice Labs runs a structured-data + visible-date + citation reconciliation across ChatGPT, Perplexity, Claude, and Google AI Overviews — applied across 100+ Nordic enterprise implementations. [Request an LLMO audit](#contact) 05 / 06 Chapter ## LLM Citation Behaviour on Stale vs Fresh Content (Qualitative) In short Across audits of ChatGPT Search, Perplexity, and Google AI Overviews, fresh sources are consistently preferred for time-sensitive queries — and the freshness threshold appears tighter than in traditional SERPs. This is a publicly observed pattern; specific lift percentages vary by query and engine, so we treat the relationship as qualitative, not quantitative. We do not claim a specific percentage uplift from freshness alone — published research isolates that variable poorly. What we do observe consistently across LLMO audits is a behavioural pattern. **Patterns observed in citation audits.** - **Time-sensitive queries.** Sources older than 12 months are routinely passed over for "current state of X" queries in ChatGPT Search and Perplexity, even when older pages still rank well in Google's blue links. - **Date-conditioned answers.** When a model produces "as of \[date\]" framing, it tends to choose sources whose visible date is close to that frame — not earlier than it. - **Statistical claims.** Models prefer sources published within ~24 months of the cited statistic. Older numbers get hedged with "older estimates suggest …". - **Definitional content.** Freshness matters less for definitions and glossary terms — these can be older without being filtered out. **The compounding effect with citations.** Aggarwal et al. (2024) showed citation-rich content can lift generative engine visibility by up to 40%. In our audits, the citation effect and the freshness effect compound: a fresh, citation-rich page consistently out-cites a fresh, citation-light page. **SparkToro's 2024 zero-click study** reported roughly 60% of searches end without a click. The implication is that being cited is the new visibility — and being cited depends on being crawled fresh. We deliberately avoid quoting a specific freshness lift. The honest framing: freshness is a qualifier, citations are an amplifier, and the two together drive most LLMO outcomes. Up to 40% lift from citation-rich content Aggarwal et al. (2024) found that citation-rich content lifted generative-engine visibility by up to 40%. Pairing fresh dates with strong citations is the highest-leverage combination we see in audits. arXiv:2311.09735 ### Want to discuss how this applies to your organization? Book a free 30-minute strategy call with our AI team. [Book a call](/en/ai-consulting-services#contact-form) 06 / 06 Chapter ## The Alice Labs Review Cadence: A Proprietary Methodology In short Alice Labs runs a documented 90-day default cycle across the LLMO content stack, with shorter cycles for high-decay content types and event-driven triggers for regulatory and pricing pages. Every article carries a visible Published / Last reviewed / Next review block, mirrored in Schema.org JSON-LD, and a quarterly LLMO Citation Benchmark verifies that citations move with the cadence. The Alice Labs review cadence is the operational layer that turns "freshness matters" from a slogan into a process. It runs across 100+ Nordic enterprise implementations. **Five components.** 1. **Default 90-day cycle.** Every evergreen article has a nextReviewDue set to publishDate + 90 days. The date is public and visible above the fold. 2. **Tier overrides.** Pricing, regulatory, and statistics roundups are tagged with tighter cycles (30, 30-60, 90 days plus source-trigger). 3. **Event triggers.** Major rule changes, competitor launches, and referenced-source updates pull a review forward regardless of the schedule. 4. **Visible + structured dates.** Published, Last reviewed, Next review due — rendered in HTML and mirrored in Schema.org datePublished / dateModified. 5. **LLMO Citation Benchmark.** Quarterly audits across ChatGPT, Perplexity, Claude, and Google AI Overviews to confirm that citation share moves with the cadence — not just the rank. **What we measure each cycle.** - Citation share per target query in each generative engine. - Crawl frequency by AI user-agent (GPTBot, ClaudeBot, PerplexityBot, OAI-SearchBot). - Visible and structured-data dates — are they reconciled? - External and internal link health. - Whether referenced statistics or sources have been superseded. **What we deliberately do not do.** We do not bump dateModified for typo fixes. We do not auto-rewrite content on a timer. We do not promise a freshness uplift in percent terms. Cadence is a discipline; outcomes follow when content quality is already strong. Start with the visible block If you do nothing else, add a 'Published / Last reviewed / Next review' block above the fold on your top 20 pages. It is a 1-day project, costs nothing, and signals an active maintainer to both humans and LLMs. ## About the Authors & Reviewers Published May 6, 2026 · Updated May 15, 2026 Written by ![Linus Ingemarsson - Co-Founder, Alice Labs at Alice Labs](/images/linus-ingemarsson.png) [Linus Ingemarsson](https://www.linkedin.com/in/linus-ingemarsson/) Co-Founder, Alice Labs Co-Founder at Alice Labs. Author of 7 research reports on AI adoption, governance and labor markets cited across EU, OECD and US benchmarks. - 8+ years in AI strategy & implementation - Top-5 AI Speaker, Sweden (Mindley 2025) - 100+ enterprise AI engagements [View profile](https://www.linkedin.com/in/linus-ingemarsson/) [](https://www.linkedin.com/in/linus-ingemarsson/)[](mailto:linus@alicelabs.ai) Reviewed by May 15, 2026 ![Eric Lundberg - Co-Founder, Alice Labs at Alice Labs](/images/eric-lundberg.png) [Eric Lundberg](https://www.linkedin.com/in/eric-lundberg-3530451bb/) Co-Founder, Alice Labs Co-Founder at Alice Labs. Builds AI automation, agent workflows and integration systems that hold up in real business operations. - AI automation & agent systems lead - Workflow design across 100+ deployments - Specialist in RAG, integrations & APIs [View profile](https://www.linkedin.com/in/eric-lundberg-3530451bb/) [](https://www.linkedin.com/in/eric-lundberg-3530451bb/)[](mailto:eric@alicelabs.ai) Published May 6, 2026 · Updated May 15, 2026 Reviewed for technical accuracy, methodology and source integrity. · All claims trace to public sources cited in-line. ## Frequently Asked Questions ### Is content freshness actually a ranking signal in 2026? Yes. Google publicly documented freshness as a ranking signal with the 2011 Freshness Algorithm Update, and it remains in effect for queries Google interprets as time-sensitive. AI search engines — ChatGPT Search, Perplexity, Google AI Overviews — also prioritise recent sources for time-sensitive queries. Freshness is not the only signal, but for many queries it is a qualifying threshold rather than a tie-breaker. ### What is the difference between datePublished and dateModified? datePublished is the date an article was first published — set once, never changed. dateModified is the date of the most recent meaningful update. Both are Schema.org Article properties and should be ISO 8601 strings (YYYY-MM-DD) inside JSON-LD. Update dateModified when content changes substantively; do not bump it for typo fixes or boilerplate edits. ### How often should I update my content? It depends on content type. News content needs review every 7-14 days. Pricing pages every 30 days. Regulatory and compliance content every 30-60 days plus event triggers. Evergreen deepdives and pillar pages every 90 days — the Alice Labs default. Glossary and definitional content can stretch to 180 days. The cycle should be documented; surprise refreshes are less effective than disciplined cadence. ### Will updating dateModified without changing content help my rankings? No, and it can hurt. Search engines and LLMs detect 'date stuffing' — bumping dateModified without meaningful edits. The penalty is loss of the freshness signal entirely, sometimes for the page, sometimes site-wide. Update dateModified only on substantive edits, and surface what changed in a visible 'What's new' block when possible. ### Do LLMs read Schema.org dates or visible HTML dates? Both. Generative engines parse JSON-LD structured data and the rendered HTML. If JSON-LD dateModified says 2026-05-06 and the visible byline says 'Posted 2022', the model will hedge or pick the older signal. The fix is to render both from the same source of truth — usually a CMS field — so they cannot drift apart. ### What is lastReviewed in Schema.org and should I use it? lastReviewed is a Schema.org property primarily defined on MedicalEntity, indicating the date a piece of content was last reviewed by a qualified expert. Outside medical content, it has no formal Article-level analogue, so most editorial sites surface 'last reviewed' as a visible UX line plus dateModified in JSON-LD. The convention is widely understood by humans and LLMs alike. ### Does adding 'Updated April 2026' to my title help? It can — for time-sensitive queries. Putting the year in the title page or H1 ('… in 2026', 'Updated April 2026') is a long-standing SEO pattern, and AI search engines pick up the same cue. The risk is staleness: when April 2026 ends, the title becomes a liability. Pair the in-title date with a real review cadence so you remember to update it. ### What is the Alice Labs review cadence? Alice Labs runs a 90-day default review cycle on evergreen content with tier overrides for high-decay types (pricing, regulatory, statistics roundups) and event triggers for legal or competitive changes. Every article carries a visible Published / Last reviewed / Next review block, mirrored in Schema.org datePublished and dateModified. We verify outcomes through a quarterly LLMO Citation Benchmark. [Previous in AI Search & LLMO ### AI Search Optimization for Agencies & Consultancies (2026) ](/en/insights/ai-search-optimization-agencies)[Next in AI Search & LLMO ### Bing Copilot Optimization: Get Cited in Microsoft AI (2026) ](/en/insights/bing-copilot-optimization) ## Further reading - [Schema.org — Article (datePublished, dateModified)](https://schema.org/Article)· schema.org - [Aggarwal et al. 2024 — GEO: Generative Engine Optimization](https://arxiv.org/abs/2311.09735)· arxiv.org - [llms.txt — Official spec, examples, and validators](https://llmstxt.org)· llmstxt.org ## Related reading [deepdive ### Schema.org for AI: Structured Data That LLMs Read Pair fresh dates with the rest of the structured-data stack — Article, FAQ, HowTo, Organization. 11 min](/en/insights/schema-org-for-ai) [howto ### Citation Optimization for AI Search: 8-Step LLMO Playbook Citation-rich content compounds with freshness — Aggarwal up-to-40% lift in generative engines. 12 min](/en/insights/citation-optimization-ai) [pillar ### AI Search Optimization: Complete Guide for 2026 Full LLMO pillar covering ChatGPT, Perplexity, Claude, and Google AI Overviews. 14 min ](/en/insights/ai-search-optimization-guide) ## Sources 1. [Schema.org — Article (datePublished, dateModified properties)](https://schema.org/Article)(accessed 2026-05-06) 2. [Aggarwal et al. — GEO: Generative Engine Optimization (arXiv:2311.09735, 2024)](https://arxiv.org/abs/2311.09735)(accessed 2026-05-06) 3. [llms.txt — Official specification, Jeremy Howard / Answer.AI (proposed September 3, 2024)](https://llmstxt.org)(accessed 2026-05-06) 4. [Google — Freshness Algorithm Update (publicly documented since 2011)](https://blog.google)(accessed 2026-05-06) 5. [SparkToro — 2024 Zero-Click Search Study (~60% zero-click)](https://sparktoro.com)(accessed 2026-05-06) Next scheduled review: 2026-08-13 ![Linus Ingemarsson](/images/linus-ingemarsson.png)![Eric Lundberg](/images/eric-lundberg.png)![Alice Holmgren](/images/alice-holmgren.png) Alice Labs practitioner team ## Talk to the team behind 100+ AI implementations 30-minute discovery call with a senior Alice Labs consultant. No slide deck, no sales pitch — just a scoping conversation. 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