Living playbook · Updated daily
AEO 101 · the playbook that ships what is working in answer engine optimization, right now.
The canonical real-time reference for AEO. Curated from our research library and the actual tactics we are running on engagements this week. Refreshed every morning.
§00 What you are reading
Compiled from our daily research of the AEO space.
Every morning our research team logs what shifted in the answer engine ecosystem the day before. Platform updates from ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews. New citation behaviors. Source-pool drift on the prompts our clients care about. Tactics that stopped working. Tactics that started working.
The page below is the synthesis. Not a static guide written once and forgotten. The platform deltas are dated from the last 14 days. The tactics are pulled from briefs our team filed this month. The deprecations are the things AEO operators used to recommend that answer engines no longer reward.
The point is simple. If you want to be the answer AI gives, your strategy has to update at the speed AI changes. Every change in the AEO space affects which brands get cited, which pages get extracted, and which sources AI trusts. This page tracks those changes and tells you what to do about them.
Platforms monitored
6
Update cadence
Weekly
Tactics on the page
16
Tactics deprecated
6
§01 What is AEO
The 60-second answer.
Answer engine optimization is the work of getting your brand named, cited, and recommended inside the answers that AI systems generate. The unit of value is no longer a ranked page. It is a piece of groundable information with clear provenance that ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews can responsibly reuse when they construct a reply.
AEO and traditional SEO share infrastructure but optimize for different outcomes. SEO asks which pages a user should visit. AEO asks what information an AI system can responsibly use to construct an answer. A page can rank well in Google and still never be cited inside an AI answer because the answer-grade evidence is buried, the schema is wrong, or the surrounding source pool quotes a stronger third party.
The operating discipline has three jobs. Engineer your own pages for passage extraction and clean retrieval. Influence the third-party pool that AI systems already cite for your category, including Reddit, G2, analyst roundups, and the comparison sites that AI fans out to. Measure citation share and recommendation rate on a fixed prompt set across every major surface, and respond inside seven days when something drifts.
This page is the live playbook. It is updated daily from our research library, our daily intel, and the actual tactics we are running on engagements this week. Dated entries you can copy. Deprecated tactics you should stop running. Platform shifts logged the day we see them.
§02 What changed this week
Platform shifts we logged in the last 14 days.
Strategy follows the platform. Every entry below is a real change one of the answer engines made, and the operator response we are running on engagements this week.
- Jun 17, 2026Google AI Overviews
Google's AI-search opt-out became enforceable on June 17, 2026. Opted-out sites stop appearing in AI Overviews, AI Mode, and AI Overviews in Discover, while organic rankings are unaffected. It is the first binding conduct requirement on Google under the UK DMCC 2024 and the first regulator-enforced AI-search opt-out anywhere, limited to a UK subset of site owners, with Gemini excluded from scope.
Advise almost all B2B clients NOT to opt out, since opting out forfeits the impression entirely. For any UK-subset client even considering it, pull an AI-impressions baseline from the GSC Gen-AI report first. Note the Gemini exclusion means you still cannot regulate your way out of multi-engine visibility.
- Jun 16, 2026Google
Google launched Search agents on June 12, 2026, starting with information agents in AI Mode for Google AI Ultra subscribers. The agents monitor news, blogs, social, finance, shopping, and sports data around the clock and push updates with links. Follow-up from an AI Overview into a conversational AI Mode session also went live worldwide.
Treat persistent citation as the new bar, since always-on agents keep re-pulling sources, so freshness and sustained authority matter more than a one-time citation. Optimize AI Overview presence as a funnel into multi-turn AI Mode sessions.
- Jun 15, 2026Cross-platform
Fractl and Search Engine Land published a study on June 15, 2026 showing consumer trust in AI search collapsed from 82% in 2025 to 54% in 2026, a 28-point drop. Buyers now check an average of 2.4 platforms before validating a purchase, trust splits Google 39% / Reddit 15% / AI tools 14%, and a cross-referenced Ahrefs 75,000-brand study found branded web mentions and YouTube impressions correlate most with AI visibility (0.50 to 0.74 Spearman) while backlinks and ad spend correlate least (under 0.30).
Lead with accuracy and credibility, not volume, and feed engines checkable sourced facts. Prioritize branded mentions and YouTube presence over backlink or ad-spend tactics, and instrument across multiple engines since buyers triangulate 2.4 platforms.
- Jun 12, 2026ChatGPT
OpenAI launched ChatGPT Ads product-feed campaigns on June 12, 2026, letting advertisers upload product feeds of up to 2 million items to power ad placements, a move toward shopping and commerce-style ads.
Apply B2B caution since this is retail/shopping-skewed, but treat machine-readable product feeds and structured catalogs as the price of entry for AI commerce surfaces.
- Jun 12, 2026Google
Adobe Brand Visibility, combining Adobe LLM Optimizer with Semrush AI Optimization data, surfaced drawing on 289M-plus AI search prompts, described as the largest global database of its kind, with reported AI traffic to US retail up 1,324% (Oct 2024 to May 2026) and travel up 2,215%.
Use the post-acquisition enterprise-suite consolidation as a qualifying point: these suites are blind to engines they do not instrument. Position vendor-neutral multi-engine triangulation as the objective alternative.
- Jun 11, 2026GA4
Google Analytics 4's native AI Assistant default channel reached broad availability around June 7, 2026 (channel introduced May 13), auto-recognizing traffic from ChatGPT, Gemini, Claude, Copilot, Grok, and DeepSeek without manual UTM setup. It still routes Perplexity to Referral, counts AI Overviews as Organic Search, and misses referrer-less AI traffic.
Enable the GA4 AI Assistant channel now to start a baseline, but pair it with server-side and own-funnel tracking since it undercounts and is blind to Perplexity and AI Overviews.
- Jun 10, 2026Cross-platform
Optimizely launched a full enterprise AEO platform on June 10, 2026 via an exclusive partnership with Conductor, embedding Conductor's SEO/GEO/AEO intelligence plus a new Agent Visibility Analytics module that uses log-level data to classify AI requests by intent (retrieval, indexing, training) and three turnkey agents (AEO Gap Finder, Competitive AI Share-of-Voice, AI Brand Visibility Report).
Build fluency in the agent-behavior and bot-log axis, not just citation share, before clients ask. Pitch reading both citation outcomes and server-log agent intent as a managed-agency advantage over single-axis dashboards.
- Jun 10, 2026ChatGPT
ChatGPT Ads was confirmed to ship native B2B conversion events in the oaiq() pixel/SDK, including lead_created (labeled best for lead gen, agencies, B2B), appointment_scheduled (service businesses, consultants), and trial_started (SaaS), closing the long-standing no-native-B2B-event measurement gap. By June 11, lead_created and appointment_scheduled appeared available for CPA bid-optimization too, though targeting remains contextual-only with no firmographic/ABM audiences and zero measured B2B performance data.
Retire the old you-cannot-even-measure-a-B2B-demo line. Stand up the oaiq() pixel plus Conversions API, fire the correct B2B event, and run a capped $2-5K/mo CPA pilot for fast-loop lead-gen/SaaS measured against the client's own Google/Meta CPA. Advise enterprise ABM clients to wait until firmographic targeting ships.
- Jun 09, 2026Google AI Overviews
SparkToro published a study on June 9, 2026 using Similarweb's US desktop and mobile clickstream panel from January through April 2026, finding that 68.01% of US Google searches now end without a click, up 7.56 percentage points from 60.45% in 2024. Of every 1,000 US Google searches, only 276 clicks now reach the open web, down from 374 in 2024. Google AI Overviews, appearing on more than 20% of all searches, were identified as the primary driver and found to cut click-through rates by nearly 60%.
With 276 of every 1,000 Google searches reaching the open web, the organic referral channel is structurally smaller than it was two years ago and the gap is widening. Treat citation inside the AI answer as the primary visibility unit for any query where an AI Overview is likely to appear. In budget conversations, use the 276-per-1,000 figure to show why optimizing for blue-link position alone is an increasingly incomplete brief. Track GSC Gen-AI impression share as a separate KPI from organic clicks, since the two now move on independent tracks and a page can rank without being cited.
- Jun 09, 2026Cross-platform
GNW Consulting and Demand Metric published the 2026 State of GEO in B2B Marketing on June 3, 2026 (n=225 B2B marketing and revenue leaders). It found 92% experimenting with or operationalizing GEO, 78% of investors reporting measurable ROI, and 22% saying AI traffic exceeds 5% of site traffic, but 88% of SEO agencies claim GEO services with 37% loosely defined, and fewer than 15% of companies have a dedicated GEO owner.
Use the demand-rich, ownership-poor, GEO-washed-supply gap as the core managed-service pitch: position Cite as the defined owner of the function against a market where almost no one owns it and a third of agency offerings are vague.
- Jun 09, 2026Cross-platform
First Page Sage published an AI-chatbot market-share estimate dated June 5, 2026 showing ChatGPT fell to 53.1% from about 61.8% the prior period, with Claude 21.1%, Gemini 13.1%, Copilot 8.7%, and Perplexity 2.7%. The figures are modeled estimates and the ChatGPT decline is the defensible finding.
Reinforce a multi-engine posture in client recommendations: with ChatGPT sliding from roughly 62% to 53%, single-engine optimize-for-ChatGPT advice is increasingly wrong. Treat the exact percentages as directional.
- Jun 08, 2026Cross-platform
First Page Sage's ChatGPT Ads Conversion Rate report (last updated April 27, 2026) surfaced as the first independent, non-Criteo B2B benchmark, projecting B2B SaaS ChatGPT-Ads conversion at about 1.1%, below its own SEO benchmark of 2.1% and roughly even with Google Ads at 1.0%, across a 19-industry spread from 0.3% (Financial Services, about $1,500-plus CPA) to 6.0% (Higher Education, about $50 CPA). The numbers are modeled, not measured live-campaign actuals.
Counter the loud 2x-better-than-search retail claim with this independent B2B projection, and use the CPA-by-vertical table in should-we-run-ChatGPT-Ads advisories. Tell clients to instrument their own demo/trial conversion before trusting any headline multiple.
- Jun 08, 2026Google
Search Engine Journal framed the citation-to-transaction shift around Google's announcement that Chrome auto-browse reaches Android phones starting at the end of June 2026 (Pixel 10 and Galaxy S26 first), and documented eight auto-browse failure modes (client-side rendering, cookie walls, unlabeled forms, div-based buttons, modal traps, CAPTCHAs, timeouts, sign-in walls) plus a silent-loss dynamic where failed agent transactions throw no analytics signal. A June 9 correction clarified the Android rollout is gated to AI Pro and Ultra subscribers, Android 12-plus, en-US, with confirmation before sensitive tasks.
Productize an agent-transaction-readiness audit: verify real button elements, semantic form labels, and a conversion path free of modal traps, CAPTCHAs, and sign-in walls so AI agents can complete bookings. Lead client conversations with the silent-loss risk since failed agent transactions generate no error or bounce.
- Jun 06, 2026ChatGPT
ChatGPT Ads went live in the UK on June 6, 2026, the first market served outside the US, Australia, and New Zealand, with expansion to Japan, South Korea, Brazil, and Mexico slated next. OpenAI projects about $2.5B in 2026 ad revenue targeting $100B by 2030, with ads serving only logged-in adult Free and Go users in shopping, retail, and travel categories.
Flag to clients that paid AI placement is internationalizing and strategically funded, but it is consumer-tier and retail/travel-skewed with zero measured B2B CPA, so the fast-loop lead-gen pilot stance holds and enterprise ABM should wait.
- Jun 05, 2026ChatGPT
OpenAI launched conversion-based billing inside the ChatGPT Ads Manager on June 5, 2026, replacing impression-only and click-only pricing with a pay-per-action model. Advertisers now pay when a user completes a defined action. The platform ships no native book-a-demo conversion event, so B2B advertisers must configure a custom action.
Before activating the new billing model, define a conversion event that reflects real purchase intent. B2B SaaS operators cannot use a purchase pixel and must pick a proxy action such as a pricing page visit or demo form submission. Retailers tied to checkout events can activate more directly. Verify that your conversion math closes at ChatGPT audience costs before committing budget.
- Jun 04, 2026Cross-platform
Burson published 'The Credibility Paradox' on June 4, 2026, the first large study to separate AI visibility from AI believability, conducted in partnership with Profound across 85 companies on 7 AI answer platforms and generating 55,000-plus believability forecasts. Fact-based claims tied to innovation, products, and workplace culture scored as the most believable across every industry studied. Claims tied to leadership, governance, and citizenship scored as the least believable. Business decision-makers rated AI-generated answers 10% more believable on average than the general population.
Citation share is a partial metric: an AI answer that surfaces your brand with a claim your audience does not believe is worse than no mention, and business decision-makers are the audience most likely to act on what AI says. Build every AEO page around sourced, checkable, fact-based claims such as product specifications, measurable outcomes, and dated case-study results. Retire soft reputation narratives about leadership vision or citizenship from the pages your citation program depends on, because those are exactly the claim types that land with the lowest believability among the buyers who trust AI answers most.
§03 Tactics working right now
16 tactics our team is running on live engagements this month.
Dated. Sourced. Replicable. Each card lists the date we validated the tactic, the category it belongs to, and the concrete steps to ship it.
Size the tracked-prompt budget per topic, not per program
Prompt allowances are sold per program while convergence thresholds are published per topic-engine pair, so entry tiers that look thin on data are actually complete reads of one topic and blind to the rest.
How
List the distinct buying conversations an AI answer could name you in, budget 25 to 40 prompts for each one, and fund the topic closest to revenue in full before adding a second.
Size the prompt-run budget per engine, not per program
Engines cite at different rates (97.4% AI Mode vs 79.1% Gemini), so equal runs across engines silently under-samples the one that cites least and makes it look like your least stable surface.
How
Set a target of 40 cited answers per topic-engine pair, then divide by that engine's cited-answer rate to get the run count: 42 runs on Google AI Mode, 44 on ChatGPT, 51 on Gemini. Report the run count next to every mention rate.
Price a tracker's market list in prompts before picking a tier
Working in August 2026 because per-country AI answers draw on different source pools, and entry tiers sold on market breadth do not hold enough prompts to cover even one market properly.
How
Multiply your buyer prompt set by the number of markets that survive a manual ten-prompt test in each country, then buy the cheapest tier that clears the total and covers the engines that localize there.
Audit which three AI engines your buyers use before picking a tracker's included set
Trackers gate engine coverage at three on self-serve tiers, and the engines disagree hard enough that the choice decides the data: Google AI Overviews and AI Mode share only 13.7% of their citations.
How
Run your ten highest-intent buyer prompts by hand across all six mainstream engines, log where you appear and which domains each cited, then buy the tracker seats that match what you found instead of the vendor's default three.
Split gated assets into an open evidence page and a gated artifact
Working in August 2026 because no AI crawler submits a form or executes JavaScript, and in software categories the citation pool runs on company-operated pages rather than earned media.
How
Pull the figures, method, and named outcomes out of each gated PDF, publish them as HTML on their own URL with the answer in the first 60 words, and leave the form on the designed file or tool below it.
Set a category noise band before reacting to any AI visibility movement
Working in July 2026 because per-category leader churn ranges from 0% to 48.4% of day pairs, so a single market-wide volatility benchmark misreads most categories.
How
Run your tracked query set daily for four to six weeks with no content changes, record the spread in citation share, and treat anything inside that spread as noise rather than a result.
Divide a tracker's monthly response cap by prompts and engines before buying a tier
AI visibility dashboards report point estimates with no confidence band, so in July 2026 the only way to know whether a weekly move is real is to check how many answers produced it.
How
Take the vendor's published responses-per-month, divide by prompts tracked and by engines covered, and compare the result against the 33 to 94 answer convergence range before you sign; negotiate response allowance ahead of engine count.
Split answer-time fetches out of AI crawler logs before reporting
Working right now in July 2026 because total AI bot volume is dominated by training crawlers that can never cite you: in our own 7-week log of 761,885 hits, only 3% were answer-time fetches and that line fell 77% while the headline rose tenfold.
How
Classify every AI user agent in your edge or server logs into training, index, and answer-time, write them to a persistent store, and chart only the answer-time line per vendor per week.
Track AI Overviews, AI Mode, and Gemini as three separate scoreboards
The three Google surfaces overlap on only 27% to 40% of brand mentions, so a blended Google AI number hides the surface you are actually losing.
How
Run a fixed set of 20 buyer prompts three times each against AI Overviews, AI Mode, and the Gemini app on a fixed weekly day, logging which surface cited you and every domain it pulled from.
Score recommendation rate separately from mention rate on every tracked prompt
Working in July 2026 because dashboards report mention rate while buyers act on the recommendation, and the two fail for different reasons: AthenaHQ measured 17.2% average brand appearance against 56.7% for category leaders, and most of that gap is selection rather than presence.
How
Run your top 20 buyer prompts and score each answer twice: were you mentioned, and were you the brand recommended. Two columns, 20 rows. A healthy mention rate with a near-zero recommendation rate means the fix is off-site source presence, not on-page structure.
Map the query fan-out, not the keyword
Agentic search runs decompose one question into ten to fifteen searches and verify claims across sources, so coverage of the sub-questions beats a single overview page.
How
Write out the sub-questions each high-value buyer prompt decomposes into (pricing, security, migration effort, integrations, support terms), then give each one its own page with a 40 to 60 word answer, a named source, and a date.
Optimize for the citation, not the click
68% of searches now end without a click, so being quoted inside the answer is the only visibility left on most queries.
How
For your top buyer prompts, run them across ChatGPT and AI Overviews, record whether your brand is cited, and track citation share as a standalone channel instead of sessions.
Set a content refresh cadence by page type so cited pages never age out
AI weights recency and cited pages decay in weeks (AirOps: 83% of commercial citations are under 12 months old), so a cadence holds citations a publish-and-forget library loses.
How
Refresh commercial pages monthly, pages you're already cited for every 30-60 days, and data pages quarterly, then re-test the buyer prompts after each update to confirm you held the citation.
Optimize for both DeepSeek modes
DeepSeek answers from training memory by default and only fetches the live web when Search mode is on, so winning it needs both corpus presence and quotable pages.
How
Build durable, well-referenced open-web presence so default DeepSeek recalls your brand from training memory, and keep pages crawlable with 40-60 word answer blocks so Search mode can fetch and quote them.
Syndicate a structured press release built around one quotable stat
A July 2026 Notified/PRWeek study found 99.3% of syndicated releases get cited by ChatGPT or Claude in about 8 hours, but the citation is perishable so it needs refresh.
How
Lead the release with a 40-60 word factual answer and one net-new first-party number, then push it through a wire AI already crawls, not just your newsroom.
Allow Meta-WebIndexer for Meta AI citations
Meta AI now grounds answers in its own crawl, so a page its indexer cannot reach cannot be cited on a surface with ~1B monthly users.
How
Audit robots.txt for blanket AI-bot blocks and explicitly allow Meta-WebIndexer, the crawler Meta names as the one that lets Meta AI cite and link to your content.
§04 The 12-step playbook
If you want to do AEO yourself, this is the order.
Each step is independent enough to ship in a week. Run them in sequence across a focused 90-day program. Skip none of them.
Step 01
Run a citation baseline across all five surfaces
Before optimizing anything, see what AI actually says about your brand. Most teams skip this and end up working from intuition. The baseline anchors every later decision.
- · Query 50 to 150 prompts in your category across ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews
- · Log citation share, recommendation rate, and the cited URL for every result
- · Save screenshots and timestamps so drift becomes obvious week over week
- · Flag every prompt where a named competitor wins and your brand is missing
Step 02
Curate a fixed prompt set that decides your category
A working set of 80 to 200 prompts that decide the sale in your category. This becomes the source of truth for every metric you report. You cannot maintain the page without it.
- · Pull buyer prompts from sales calls, support tickets, and the public Profound or PromptWatch research data
- · Cover four families: category education, vendor shortlist, feature comparison, post-purchase
- · Map each prompt to the page that should answer it
- · Lock the list. Treat additions as a change request, not an edit
Step 03
Audit retrieval before you touch content
If the answer block, schema, or internal links only exist in the hydrated DOM, AI retrieval cannot rely on them. Fix the technical floor first or every later optimization compounds the wrong way.
- · Check robots.txt is not blocking GPTBot, ClaudeBot, PerplexityBot, or Google-Extended
- · Run an HTML parity audit on every priority page: initial HTML vs hydrated DOM
- · Verify FAQ, HowTo, Article, Product, and Organization schema render in the source
- · Test rendering on a slow connection: if the answer block needs hydration, it is unreliable
Step 04
Engineer answer blocks at the top of every priority page
44 percent of AI citations come from the first 30 percent of the text. Put the answer first, then the supporting evidence. This is the single biggest content lever and it is the cheapest one to ship.
- · Open every priority page with a 40 to 60 word direct answer block
- · Follow it with three to five supporting bullets that include numbers and named sources
- · Add a structured FAQ section with at least four questions and FAQ schema
- · Strip marketing copy that buries the answer further down the page
Step 05
Apply correct schema to the surfaces AI actually reads
FAQ schema lifts citation by a measured 350 percent in controlled studies. Article, HowTo, Product, and Organization schema set entity coherence. Generic schema without entity coherence is now a deprecated signal.
- · Pick the right schema per page type: Article for editorial, HowTo for playbooks, Product for SKUs, Service for offers, FAQPage for FAQ blocks
- · Use Organization schema with a single canonical sameAs set across LinkedIn, Crunchbase, and your site
- · Validate every page in Schema.org and Google Rich Results
- · Re-check schema after every deploy. Frameworks strip it more often than teams realize
Step 06
Build buyer-stage pages around the prompt families you mapped
AI cites pages that answer the specific evaluation question, not generic category pages. Each prompt family needs a dedicated asset.
- · Comparison pages: feature matrix, three tables, named competitors
- · Pricing pages: formulas visible, scenario ranges, plan limits stated
- · Trust center: SOC 2 and ISO with auditor and date, not gated PDFs
- · Use case pages: one per job-to-be-done, with named role and operating context
Step 07
Get into the third-party pool AI already cites
AI rarely cites your own site for category-defining queries. It cites Reddit, G2, analyst roundups, and the comparison sites already in the source pool. The work is making sure your brand is represented there.
- · Identify the 10 to 20 domains AI cites most for your category prompts
- · Pitch contributed pieces, expert quotes, or product listings to each one
- · Seed Reddit threads with practitioner answers, not promotional copy
- · Maintain G2 and Capterra listing freshness: response rate, feature checks, version notes
Step 08
Engineer citation presentation, not just citation share
Google now adds subscription labels, community labels, author names, inline placement, and hover previews to AI citations. Click confidence is the new metric. Showing up is no longer the whole game.
- · Set canonical brand name, favicon, and Organization schema so the source label is clean
- · Publish bylined practitioner content on LinkedIn and Medium for author-name treatment
- · Tighten the meta description and the first 60 words on every priority page
- · Connect Google subscription linking if you have a paywall or membership
Step 09
Track prompts, logs, and conversions in one model
Prompt dashboards alone miss what crawlers do and whether the traffic converts. The measurement stack has split into three layers. Each one needs its own instrumentation.
- · Run the fixed prompt set every week and log citation share, recommendation rate, and source URL
- · Ingest CDN or Cloudflare logs to see GPTBot, ClaudeBot, PerplexityBot, and Google-Extended traffic
- · Wire the OpenAI Conversions API plus pixel measurement for paid ChatGPT traffic
- · Land all three layers in one weekly readout so the team can see cause and effect
Step 10
Maintain a change log that connects releases to prompt outcomes
Most teams can see movement but cannot explain it. A durable log of what changed, when, and which prompt family it was meant to affect makes root-cause analysis weeks faster.
- · Log every content update, schema change, page ship, and outreach placement
- · Record day-one, day-seven, and day-thirty observations on the affected prompt family
- · Tie each change to a named owner so accountability is concrete
- · Review the log at the start of every weekly readout
Step 11
Course-correct within seven days when something drifts
AI source pools have 40 to 60 percent monthly churn. Drift is normal. The discipline is responding to it inside the week, before a competitor cements the new pool position.
- · Set an alert threshold on citation share by prompt family
- · When a domain enters or exits the cited pool for a priority prompt, diagnose within 24 hours
- · Ship a content refresh, schema fix, or publication outreach response inside seven days
- · Document the response in the change log so the pattern compounds
Step 12
Run a quarterly contradiction and page-collision audit
AI quotes the clearest available claim. When pricing, implementation, support, or product detail pages disagree, AI picks one and runs with it. Quarterly cleanup keeps the source pool from feeding bad answers.
- · Inventory every public claim on pricing, plan limits, support, and product detail
- · Classify conflicts: outright contradiction, stale stat, ambiguous scope, missing context
- · Assign a source of truth and propagate it across the cluster
- · Retest the affected prompt family the same week and log the result
§05 Tactics we deprecated
These used to work. They do not anymore.
Stop running these. Migrate to the live tactics in §03 or to the replacements listed below.
- Markdown mirror pages for AI crawlersApr 10, 2026Otterly 14-day controlled experiment: AI crawlers visited HTML pages but recorded zero visits and zero citations against the Markdown mirrors across ChatGPT, Perplexity, AI Overviews, and Claude.Server-side rendered HTML with clean schema
- Hidden text to seed AI with extra contextApr 09, 2026Four of six platforms ignore hidden text entirely. Copilot flags pages with hidden text as unsafe. Gemini actively reports prompt injection attempts.Visible answer blocks at the top of each section
- Self-promotional best of listiclesMar 15, 2026Google explicitly cracked down on this pattern in 2026 core updates, with reported 30 to 50 percent visibility drops on offending pages.Comparison content with named competitors and structured feature matrices
- Generic FAQ schema without entity coherenceMar 01, 2026FAQ schema lifts citations only when the questions match real prompts and the Organization schema, sameAs set, and author entity all agree. Generic FAQ alone no longer signals quality.FAQ schema tied to prompt families plus Organization and Article schema with a clean entity set
- Keyword density and exact-match anchor textFeb 01, 2026AI retrieval scores passage clarity and source corroboration, not keyword frequency. Exact-match anchors signal manipulation to most modern crawlers.Natural anchor variation and 40 to 60 word direct answer blocks per section
- llms.txt as a citation leverApr 05, 2026SE Ranking analysis of 300,000 domains found no measurable impact of llms.txt on citation frequency. Treat as hygiene, not a lever. Only 10 percent of sites have one and not a single top-1,000 site has implemented it.Server-side rendering, schema, and answer-block engineering
§06 How we maintain this page
Live, because the platforms move every week.
We track 6 answer engines on a continuous schedule. Every change we observe goes into our internal Brain, the same research library the AI Visibility Index publishes from.
Every morning, the latest validated tactics, platform deltas, and deprecation calls are reflected here. Nothing on this page is older than this week unless it is in §05 for a reason.
Platforms tracked
Prompts tracked
1,500+
Across all engagements, refreshed weekly. Last sync Aug 10, 2026.
Cadence
Weekly prompt sweep, daily page refresh
Tactics validated in the last 30 days: 16. Deprecations issued: 6.
§07 Want us to do this for you?
If you would rather not run this yourself, that is why we exist.
We run the playbook as a managed service. Pilot pricing on agreed outcomes. You pay for tools and APIs during the pilot and a success fee only if we hit the goal in the engagement letter.
Ready to become the answer AI gives?
Book a 30-minute discovery call. We'll show you what AI says about your brand today. No pitch. Just data.