AI Summary
Anchor text is the primary semantic label an AI retrieval system reads when it maps your domain’s topic graph. Every internal link is a vote that says “that page is about X.” Generic anchors like “click here” or “read more” cast a blank ballot. Descriptive anchors cast an informed one, and over dozens of links across a cluster the difference in topic-coherence signal is measurable.
This post owns the anchor text rules that our internal linking for AI search guide delegates to it. The mechanics start with how AI retrieval systems read link graphs, then move to variation rules, anchor rules by link type, the audit method, and what to avoid.
How AI retrieval systems read anchor text
Classic PageRank treated internal links as authority tokens. AI retrieval systems treat them as entity labels. When a content chunking and RAG retrieval pipeline traverses your domain, it reads the anchor on each internal link and stores a relationship: source page topic, anchor phrase, destination page. After dozens of traversals, a destination page accumulates a label cloud from every anchor that points at it.
A page with 20 inbound links anchored “content gap analysis for AI search” has a strong label. A page with 20 inbound links anchored “here,” “this article,” and “learn more” has no label. It may rank well in classic organic search through other signals, but it is invisible to topic-graph-based retrieval. The practical implication: anchor text is on-page optimization work, not just link-building work. You control every internal anchor.
Our citation velocity framework typically shows anchor-text fixes registering in AI search analytics within four weeks of a systematic anchor pass.
The anchor variation principle
The internal linking guide states this rule: the same anchor in five different sentence contexts teaches the AI five semantic facets of the linked page. This is the anchor variation principle, and it has two parts that are in tension if you do not understand both.
Entity consistency. Every anchor must identify the same entity. If a page is about content gap analysis for AI search, anchors pointing there should reference that entity. “AI content gap analysis,” “content gaps in AI search,” and “finding gaps competitors miss in AI engines” all identify the same entity. “This post” does not.
Phrasing variation. Using the exact same six-word phrase in every anchor is a manipulation signal and provides only one semantic facet. Vary the phrasing while keeping entity consistency. Use the full entity name sometimes, a partial match sometimes, a question form sometimes, and a brand reference sometimes. The combination produces a label cloud that is rich, natural, and unreplicable by manipulation.
Anchor rules by link type
Not all internal links serve the same purpose. The anchor rules differ by link type. Here is the framework we apply across client sites.
Pillar to cluster links
These links point from a broad pillar and cluster content page down to a narrower cluster page. The anchor should name the cluster page’s specific topic. Do not use the pillar’s own keyword as the anchor on the outbound link. The pillar is the hub; the anchor names what the spoke covers. A pillar on “GEO for B2B SaaS” linking to “AI citation tracking” should anchor as “AI citation tracking methods,” not as “GEO.”
Cluster to pillar uplinks
Every cluster page should link back up to its pillar. The anchor on an uplink should use the pillar’s primary keyword at least 50 percent of the time across all cluster pages. When half the cluster page anchors say “GEO for B2B SaaS” and point at the pillar, the pillar registers as the canonical answer to that topic. Vary the remaining 50 percent by sentence context, not by entity. The internal linking architecture guide has full uplink-count targets per tier.
Lateral links between cluster peers
Lateral links connect cluster pages that cover adjacent subtopics. They tell the AI that two pages are semantic neighbors, not the same topic. The anchor on a lateral link should identify what makes the destination page different from the source. If a cluster page on schema markup for AI search links laterally to content chunking, the anchor should read “content chunking for AI retrieval” not “this related topic.”
Deep-dive delegation links
Some posts intentionally delegate a topic to another post. The internal linking guide delegates anchor rules to this page with explicit language: “our guide on anchor text for AI search covers the full rules.” That delegation is a signal to AI retrieval systems that this page is the canonical deep-dive. For a delegation link, the anchor should name the scope being delegated: “our full anchor text selection rules” or “the complete anchor variation framework.” Vague anchors like “more on this” defeat the delegation signal entirely. The atomic sentence structure guide uses the same delegation pattern for sentence-level rules.
What to avoid: generic anchors and over-exact-match
There are two failure modes, and most sites have both simultaneously.
Generic anchors are the larger problem. “Click here,” “read more,” “this guide,” “here,” and “learn more” provide zero topic signal. When our GEO content audit extracts all internal anchors for a client site, generic anchors typically account for 20 to 50 percent of the total. On a blog with 100 posts and 6 internal links per post on average, that is 120 to 300 blank ballots cast against the topic graph.
Over-exact-match anchors are the less common but still damaging pattern. When every inbound link to a page uses the exact same keyword phrase, both classic ranking systems and AI systems treat it as a manipulation signal. The variation principle is partly about semantic richness and partly about natural language. Real editorial linking produces varied phrasing. Perfect uniformity does not occur naturally, and retrieval systems are calibrated against that expectation.
The target distribution for a well-optimized page: 40 to 60 percent of anchors use the primary entity with phrasing variation, 30 to 40 percent use partial or related phrases, under 5 percent are generic, and the rest use question forms or brand references. This mirrors the natural distribution on topically authoritative sites.
Audit method: extract anchors per destination URL
The audit goal is simple: for each destination URL, know the distribution of anchors pointing at it. The code below processes a Screaming Frog “All Internal Links” CSV export.
# anchor_audit.py
# Input: Screaming Frog "All Internal Links" export as CSV
# Required columns: Source, Destination, Anchor
import csv
from collections import defaultdict, Counter
GENERIC = {"click here", "here", "learn more", "read more",
"this", "this guide", "this article", "more", "link"}
def audit_anchors(filepath):
dest_anchors = defaultdict(list)
with open(filepath, encoding="utf-8") as f:
reader = csv.DictReader(f)
for row in reader:
dest = row["Destination"].strip()
anchor = row["Anchor"].strip().lower()
dest_anchors[dest].append(anchor)
results = []
for dest, anchors in dest_anchors.items():
total = len(anchors)
counter = Counter(anchors)
generic_count = sum(v for k, v in counter.items() if k in GENERIC)
generic_pct = round(generic_count / total * 100, 1) if total else 0
top_anchor = counter.most_common(1)[0] if counter else ("none", 0)
results.append({
"url": dest,
"total_links": total,
"generic_pct": generic_pct,
"top_anchor": top_anchor[0],
"top_anchor_pct": round(top_anchor[1] / total * 100, 1) if total else 0,
})
results.sort(key=lambda x: x["generic_pct"], reverse=True)
print(f"{'URL':<55} {'Total':>6} {'Generic%':>9} {'Top anchor':<35}")
print("-" * 110)
for r in results[:30]:
print(f"{r['url'][:55]:<55} {r['total_links']:>6} "
f"{r['generic_pct']:>8}% {r['top_anchor'][:35]:<35}")
if __name__ == "__main__":
audit_anchors("all_internal_links.csv")Pages with generic_pct above 20 percent are priority fixes. Sort by total_links descending to tackle your most-linked destinations first. In client work, most generic-anchor problems trace back to two sources: navigation templates and one or two authors who default to "click here." The template fix is a one-time edit; the author fix is a style-guide update. Neither requires touching individual posts. Our GEO audit service surfaces both sources in the first session.
For a full picture of how anchor text interacts with the rest of the GEO signal stack, including outbound link trust signals and link building for AI search, our 50-point GEO audit checklist scores anchor quality as one of its dedicated dimensions.