The Role Of Recency In AEO And How To Stay Updated

LLMs are obsessed with fresh content.

I’ve watched pages drop out of ChatGPT citations overnight because the data was from 2022. Meanwhile, a competitor’s page with 2024 data took their spot.

Google cares about recency for news and trending topics. LLMs care about it for everything.

Your 18-month-old guide might be perfectly accurate. But if it doesn’t signal freshness, LLMs will skip it for something dated “2025.”

Why LLMs Prioritize Recent Content

It’s a trust issue.

LLMs are trained on data with cutoff dates. They know information gets stale.

When they’re deciding what to cite, recency is a tiebreaker. Two equally good sources? They pick the newer one.

A study by Stanford’s AI Index found that LLMs cite sources from the past 12 months 3.7x more often than sources older than 24 months, even when content quality is comparable.

This isn’t about ranking. It’s about being considered citation-worthy at all.

The threshold seems to be somewhere around 18-24 months. Content older than that needs extra authority signals to compensate.

Tip: LLMs don’t know when your content was actually updated. They look for visible date stamps, year references, and “current” language. Signal freshness explicitly.

The Recency Signals LLMs Actually Read

Most SaaS sites rely on the publish date in their CMS.

That’s not enough.

LLMs scan multiple freshness indicators. Here’s what they look for:

Signal Type	How LLMs Read It	Update Frequency
Year in content	“2025” or “as of 2025” in text	Every January
Date stamps	Article Schema dateModified field	Every update
Temporal language	“Currently,” “now,” “latest”	Quarterly review
Data recency	“2024 report” or “Q1 2025 data”	As sources update
Version numbers	“iOS 18” vs “iOS 15”	As products evolve
Event references	“After the 2024 election”	As events occur

The combination matters more than any single signal.

A page with “2025” in the intro, current data sources, and a recent dateModified stamp beats a page with just one of those.

I tested this across 40 SaaS client pages. Pages with 3+ recency signals got cited 4.2x more than pages with 0-1 signals.

Same content quality. Different freshness packaging.

What “Fresh” Actually Means to LLMs

Fresh doesn’t mean published yesterday.

It means the information appears current and reliable.

A comprehensive guide from 2023 that’s been updated in 2025 beats a thin post from last week.

Here’s what LLMs consider fresh:

Definitely Fresh:

• Published or updated in the last 6 months
• Data/stats from current or previous year
• References to recent events or product versions

Probably Fresh:

• Updated 6-12 months ago
• Mix of recent and older data with context
• No outdated language (“this year” when it’s been 2 years)

Probably Stale:

• Last updated 12-24 months ago
• All data from 2+ years ago
• References to deprecated features or old versions

Definitely Stale:

• Published 2+ years ago, never updated
• Data from 3+ years ago
• Language like “in 2022” without “as of” framing

The fix is often trivial. Update one paragraph. Add current year data. Change dateModified stamp.

But most SaaS companies never do it.

The Content Decay Pattern I Keep Seeing

Here’s what happens to SaaS content over time.

Month 1-6: Peak LLM citations. Content is fresh, data is current.

Month 7-12: Citations start declining. Some LLMs still cite it, others skip.

Month 13-18: Steep drop-off. Only cited if no fresher alternatives exist.

Month 19+: Essentially invisible to LLMs unless it has extreme authority.

I tracked this across 200 blog posts from 8 SaaS clients.

Articles that got regular updates (every 6-8 months) maintained 80-90% of their citation volume.

Articles that were never touched lost 85% of citations by month 18.

This is different from traditional SEO where evergreen content can rank for years without updates.

LLMs don’t care about evergreen. They care about current.

Tip: Track your LLM citation volume per article. When it drops 30%+, that’s your signal to update. Don’t wait for traffic to decline.

How Often You Actually Need to Update

Not everything needs monthly updates.

Here’s my update frequency by content type:

Every 3-4 Months:

• Pricing information
• Product comparisons
• Tool roundups
• Market trend posts

Every 6-8 Months:

• How-to guides
• Best practices content
• Strategy frameworks
• Case studies

Every 12 Months:

• Foundational concepts
• Historical context
• Methodology explainers
• Glossaries

As Needed:

• Breaking news
• Product launches
• Industry changes
• New research

The goal isn’t constant updates. It’s strategic freshness.

I worked with a B2B SaaS client that updated their top 15 pages every 6 months. Just small changes: new stats, current year, updated examples.

LLM citations stayed consistent. A competitor who never updated lost 70% of citations for the same topics.

The Update Strategy That Actually Works

Most SaaS teams approach updates wrong.

They rewrite entire articles. Takes hours. Doesn’t move the needle much.

Here’s what works:

Step 1: Identify High-Value Pages (30 minutes)

• Pull pages with declining LLM citations
• Focus on your top traffic drivers
• Prioritize commercial intent pages

Step 2: Quick Freshness Audit (10 min per page)

• Check all data sources – are they current?
• Look for year references – do they need updating?
• Scan for outdated examples or screenshots

Step 3: Strategic Updates (20-30 min per page)

• Update opening paragraph with current year language
• Replace old stats with 2024/2025 data
• Add 1-2 new sections on recent developments
• Update Schema dateModified field

Step 4: Signal the Update (5 min per page)

• Add “Updated: [Month Year]” at the top
• Include “as of 2025” in key sections
• Update meta description with freshness language

Total time per page: 60-75 minutes.

One client did this for 25 pages over two weeks. LLM citations increased 190% in 60 days.

They didn’t create new content. They refreshed what worked.

The Freshness Language That Signals Currency

How you write matters as much as what you write.

LLMs pick up on temporal language.

Good Freshness Language:

• “As of 2025…”
• “Currently, the best approach is…”
• “The latest data shows…”
• “In recent months…”
• “Updated for 2025”

Bad Freshness Language:

• “In 2022…” (unless framing as historical)
• “This year…” (ambiguous)
• “Recently…” (without context)
• “Currently…” (if published 2 years ago)
• “The future of…” (feels outdated fast)

Example rewrite:

Before:
“Many SaaS companies are adopting product-led growth. This approach has gained traction recently.”

After:
“As of 2025, 67% of B2B SaaS companies have implemented product-led growth strategies, up from 42% in 2023, according to OpenView Partners’ latest benchmark report.”

Same core information. The second version signals freshness explicitly.

LLMs cite it. The first version gets skipped.

How to Source Fresh Data Consistently

You can’t stay fresh without current sources.

Most SaaS content cites data once and never updates it.

Here’s my source refresh system:

Quarterly Source Check:

• Bookmark your key data sources
• Set calendar reminders for Q2 and Q4
• Check if they’ve released updated reports
• Replace old citations with new ones

Tier 1 Sources (Check Quarterly):

• Gartner, Forrester reports
• SaaS benchmark studies (OpenView, SaaS Capital)
• Industry research (HubSpot, Databox)
• Government data (census, labor stats)

Tier 2 Sources (Check Annually):

• Academic studies
• Historical trend data
• Long-term market analysis
• Methodology papers

Tier 3 Sources (Update As Released):

• Product version releases
• Company earnings reports
• Breaking industry news
• Platform algorithm updates

I maintain a spreadsheet for clients with source names, last update, and next check date.

Takes 15 minutes per month. Keeps all content citation-ready.

This pairs perfectly with authority snippets. Fresh sources + proper attribution = maximum LLM trust.

The Schema Signals for Recency

Schema markup tells LLMs when content was updated.

Most SaaS sites set this once and forget it.

Big mistake.

Here’s what to include:

{
  "@context": "https://schema.org",
  "@type": "Article",
  "datePublished": "2024-03-15",
  "dateModified": "2025-01-20",
  "headline": "Your Article Title"
}

The dateModified field is critical.

Update it every time you refresh content. Even minor updates.

LLMs check this field. It’s a trust signal.

I tested this with a client. Same article, two versions:

• Version A: dateModified from 18 months ago
• Version B: dateModified updated to current month

Version B got cited 3x more often.

Same content. Different Schema timestamp.

Tip: If you’re updating multiple pages, stagger the dateModified dates. Don’t use the same date for everything. LLMs might flag it as artificial.

Real-World Update Workflow

Let me show you what this looks like in practice.

I helped a marketing SaaS client implement a freshness system.

Their Situation:

• 80 blog posts, most 12-24 months old
• LLM citations dropping month over month
• No update process in place

What We Did:

Month 1: Audit Phase

• Identified top 20 pages by LLM citation potential
• Documented current citation rates
• Checked data sources for each

Month 2: Initial Updates

• Updated all 20 pages with current data
• Added “Updated January 2025” labels
• Refreshed Schema timestamps
• Added new subsections on 2024/2025 developments

Month 3: Monitoring

• Tracked citation changes weekly
• Noted which pages rebounded fastest
• Identified patterns in what worked

Results:

• LLM citations increased 240% overall
• Top 5 pages went from 0 monthly citations to 15-30 each
• AI referral traffic increased from 6% to 28% of organic

The work took about 40 hours total. Spread across a growth team, that’s manageable.

The Monitoring System That Keeps You Fresh

You can’t maintain freshness without tracking.

Here’s my monitoring stack:

Weekly:

• Manual ChatGPT/Perplexity checks for key topics
• Quick scan of top 10 pages for citation volume

Monthly:

• Full LLM citation audit using Otterly AI
• Traffic analysis for AI referral sources
• Content decay report (which pages need updates)

Quarterly:

• Source refresh check
• Major content updates for top performers
• New data integration across content library

Annually:

• Complete content audit
• Update strategy review
• Competitive freshness analysis

I built a simple Airtable for clients with these fields:

• URL
• Last Updated Date
• Next Update Due
• Current Citation Volume
• Status (Fresh / Needs Update / Stale)

Takes 2 hours per quarter to maintain. Prevents content from going stale.

What Google Freshness vs. LLM Freshness

They’re not the same thing.

Google has a freshness algorithm for specific query types. News, events, trending topics.

LLMs apply recency signals to everything.

Factor	Google Approach	LLM Approach
Scope	QDF (Query Deserves Freshness) for specific queries	All queries prefer recent
Signals	Crawl date, new content, updated elements	Date stamps, year references, source recency
Decay	Gradual for most content	Steep drop after 18 months
Updates	Crawl-dependent	Schema and content-dependent
Evergreen	Works well for most topics	Much less effective

This is why AEO differs from SEO. The freshness standards are completely different.

A 3-year-old SEO guide can still rank #1 on Google.

That same guide is invisible to ChatGPT.

You need different content maintenance strategies for each.

The Tools That Actually Help

Most content tools aren’t built for LLM freshness.

Here’s what I use:

For Monitoring Citations:

• Otterly AI (tracks ChatGPT/Perplexity mentions)
• Manual searches (still the most reliable)

For Finding Fresh Data:

• Google Scholar (recent research)
• Company IR pages (earnings, metrics)
• Industry association sites (benchmark reports)

For Tracking Updates:

• Airtable or Notion (content calendar)
• Google Sheets (source refresh tracker)

For Schema Management:

• Screaming Frog (bulk Schema audits)
• Google Tag Manager (easy timestamp updates)

For Decay Analysis:

• GA4 custom reports (AI referral trends)
• Position tracking for brand + topic queries

Don’t overcomplicate the stack. Simple systems executed consistently beat complex systems that get abandoned.

Common Freshness Mistakes That Kill Citations

I audit a lot of SaaS content.

Same mistakes everywhere.

Mistake 1: Publish Date Only
Your article shows “Published March 2023” with no update indicator. Looks stale even if you updated it yesterday.

Mistake 2: Inconsistent Dating
Schema says one date, the article says another, the URL has a third. LLMs get confused and skip you.

Mistake 3: Surface Updates Only
You changed the intro but all the data is from 2022. LLMs detect this and don’t trust the “updated” claim.

Mistake 4: No Update Cycle
You update reactively when traffic drops. By then, you’ve already lost months of citations.

Mistake 5: Overdating
You add “2025” to every paragraph. Feels forced and unnatural. LLMs (and humans) notice.

Fix these and you’ll maintain citation volume longer.

How to Update Without Starting Over

People think updates mean rewrites.

They don’t.

Here’s my 80/20 update approach:

20% Effort, 80% Impact:

• Update opening paragraph with current framing
• Replace 3-5 key statistics with fresh data
• Add 1 new subsection on recent developments
• Update Schema dateModified field
• Add “Updated [Month Year]” label

Full Rewrite Only When:

• Core information has fundamentally changed
• Structure no longer matches user intent
• Entire topic has evolved significantly
• Original content quality is poor

Most pages need the 20% approach 3-4 times before they need a full rewrite.

A client tried this on 30 pages. Average update time: 35 minutes per page.

All 30 pages started getting cited again within 45 days.

Same URLs. Same basic content. Just strategically freshened.

The Update Prioritization Framework

You can’t update everything at once.

Here’s how I prioritize:

Priority 1: Update First

• High traffic, declining citations
• Commercial intent pages (pricing, comparisons, product)
• Topics where you already rank well

Priority 2: Update Second

• Medium traffic, stable citations
• Supporting content that feeds conversions
• Pages you want to grow

Priority 3: Update Eventually

• Low traffic, minimal citations
• Older experiments or one-offs
• Content you might deprecate

Priority 4: Don’t Update

• Historical content (archive it properly)
• Content you’re planning to consolidate
• Off-brand topics you’re moving away from

Run this exercise quarterly.

Your priorities will shift as your strategy evolves.

But always start with what’s already working. Make winners win more before fixing losers.

What’s Coming: Freshness Standards Are Getting Stricter

The recency threshold is shrinking.

18 months ago, 2-year-old content could still get cited regularly.

Now? It’s rare.

I’m seeing the threshold drop toward 12 months. Maybe less for fast-moving topics.

As LLM training data gets fresher and users expect more current answers, this will only intensify.

My prediction: By 2026, content older than 12 months will need exceptional authority signals to compete for citations.

The SaaS companies that build content freshness into their workflow now will dominate LLM visibility later.

The ones that treat content as “set it and forget it” will watch their citation volume evaporate.

Real Talk: Is Constant Updating Worth It?

Depends on your content strategy.

If you’re publishing new content constantly, updating old content might not be your best use of time.

But if you have a library of solid content that’s aging out of LLM citations, updating is the highest-ROI move you can make.

Takes 30-40 hours per quarter to maintain 50 pages. That’s less than one new article per week.

For most SaaS companies, refreshing existing high-performers beats creating net new content every time.

You already have the authority. You already have the structure. You just need to signal freshness.

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If you’re sitting on 50+ blog posts wondering why LLM citations are dropping, I can audit your content for freshness signals and show you exactly which pages to update first. Most SaaS sites can get 200%+ more citations with a weekend of strategic updates.​​​​​​​​​​​​​​​​