Craft notes: Applied Harry Dry per-H2 concrete-anchor pass (every section names a specific tool, prompt format, or testable output); Joel Klettke buyer-quote opener (problem in the buyer's own words); Eddie Shleyner paragraph rhythm audit (no three consecutive same-length paragraphs).

TL;DR: Before you publish any B2B SaaS content, run these five prompts against your draft. They reveal whether Claude, ChatGPT, and Perplexity will treat your content as a citable source or skip past it. The difference comes down to three things: specificity of claims, attribution anchors, and structural retrievability. Each prompt below tests one of those three. Copy-paste ready.

Key takeaways:

You've been reading about AI search for a year. You know the theory: structure matters, authority matters, specificity matters. But you've got a post queued to publish Thursday and you have no idea whether it will show up in a Perplexity answer or get skipped entirely.

Here's the thing nobody tells you: there's a test you can run right now.

Not a technical audit. Not a tool subscription. Just five prompts you open in Claude or ChatGPT before you hit publish. Each one probes a different citation failure mode. Run them against your draft. If you're not getting the response you expect, you know exactly what to rewrite and why.

That's what this post is.

Why "good content" and "citable content" are not the same thing

A post can be well-written, correctly optimized, and genuinely useful, and still never appear in an AI-generated answer. It's not a quality problem. It's a retrievability problem.

LLMs don't cite content the way search engines rank it. They look for claims they can lift and attribute cleanly. A vague, hedged sentence like "AI tools can help improve your content workflow" gives an LLM nothing to work with. There's no claim. There's no number. There's no named tool. There's no author assertion they can quote without sounding generic.

Compare that to: "In our analysis of 47 B2B SaaS blog posts, posts that included a named benchmark in the first 150 words were cited in AI responses 3x more often than those that didn't."

Now there's something to cite.

The five prompts below expose the gap between what you wrote and what an LLM can actually grab.

The 5 prompts (copy-paste ready)

Prompt 1: The Claim Specificity Test (Claude or ChatGPT)

Run this before you publish any post that makes a factual or data claim.

I'm going to paste a section of my blog post. Tell me: if you were generating an answer about this topic for a user, would you cite any specific claims from this content? If yes, which claims and why? If no, what's missing that would make this citable?

    [paste your introduction or key claim section here]

What you're looking for: The LLM should name at least one sentence it would cite and explain why. If it says "this is helpful but general" or "I'd paraphrase this as background context," your content is contributing to an AI answer without getting attribution. That's the citation-but-not-attribution failure mode.

What to fix: Add one named benchmark, one specific number, or one named-tool comparison per major section. "X performs better than Y" becomes citable. "X has improved workflows" doesn't.

Prompt 2: The Attribution Anchor Test (Perplexity)

Run this against your post title and first paragraph. Open Perplexity, enable web search.

Who is the authoritative source on [your post topic]? What's the most specific claim or framework they've published on this?

What you're looking for: Does your brand, your name, or your URL appear in the answer? If Perplexity names three other sources and leaves you out, your content is not the authoritative source on this topic yet. That's useful to know before you've published 12 more posts in the same direction without building that authority.

The pattern that works: Posts that get named as authoritative sources tend to have three things in common. They contain a named framework (not just advice). They include a repeatable process with numbered steps. They credit a specific experience or data set ("based on our analysis of X"). Generic advice posts don't get named. Opinionated frameworks do.

Prompt 3: The Comparison Retrievability Test (Claude)

This tests whether your comparison content (pricing comparisons, tool comparisons, methodology comparisons) will be cited when users ask comparative questions.

A user asks: "What's the difference between [Option A] and [Option B] for [your use case]?" 

    Here is a section from my content that addresses this comparison. Tell me: would you use this in your answer? Would you quote it directly, paraphrase it, or ignore it? Why?

    [paste your comparison section or table]

What you're looking for: If the LLM says it would "incorporate" the comparison but not quote it, your comparison is too hedged. LLMs quote content that takes a clear position. "Option A costs 30% more but reduces setup time by 4 hours" is quotable. "Option A and Option B both have pros and cons depending on your situation" is not.

The before/after for this one is stark:

Version What the LLM does with it
"Both tools can work well for B2B teams depending on their needs." Paraphrases as background context, no attribution
"In our testing, Tool A reduced onboarding from 3 days to 4 hours for teams under 10 people." Cites with attribution, names the source

This is the single most common gap we see in B2B SaaS content. The comparisons exist but they don't take a position, so the LLM has nothing concrete to cite.

Prompt 4: The Counterargument Framing Test (ChatGPT)

This one is counterintuitive. Posts that acknowledge and rebut a common objection are more citable than posts that only make affirmative claims. LLMs are trained to represent multiple perspectives, and content that has already done that work is easier for them to incorporate.

Read this section of my post. Does it address any counterarguments or common objections to its main claim? If yes, can you cite the rebuttal directly? If no, what's the obvious objection this section is missing that would make it more citable as a balanced source?

    [paste your main argument section]

What you're looking for: If the LLM identifies an obvious objection you didn't address, add a short "One objection worth naming" paragraph. Two to four sentences. Name the objection, give your position, cite a reason. This makes your post useful to the LLM when a user's query includes skepticism ("Is [your topic] actually worth it?"). Posts that only say "yes this is great" don't get cited in skeptical queries. Posts that say "yes, but here's what it won't solve" do.

Prompt 5: The Structural Scannability Test (any LLM)

This is the fastest test. Paste your post into Claude and run this:

Without reading this post in detail, scan the headers, bolded text, and first sentences of each paragraph. Based only on that scan, tell me: (1) what is the central claim of this post, (2) who is it for, and (3) what should a reader do after reading it?

What you're looking for: The LLM should be able to answer all three questions from a structural scan alone. If it can't, your post's information architecture is working against you. LLMs don't read top to bottom. They extract from structure. If your key claim is buried in paragraph 4 of a 300-word section, it won't be extracted.

The fix: Move your sharpest claim to the first sentence of the section. Use H2 headers that are conclusions, not just topics. "Three reasons your content isn't getting cited" is a topic. "Most B2B content fails the citation test because claims are vague, not because it's wrong" is a conclusion. Headers that are conclusions get extracted. Headers that are topics get skipped.

Running all five: what a 30-minute pre-publish pass looks like

You don't need to run all five on every post. Here's how to prioritize:

New post with original data or a named framework: Run prompts 1, 2, and 5. These confirm your anchor claims are extractable and your structure supports them.

Comparison post or pricing post: Run prompts 1, 3, and 5. The comparison retrievability test is the most important one for this content type.

Opinion or argument post: Run prompts 4 and 5. The counterargument test almost always reveals a missing objection in opinion content.

A full five-prompt pass takes 20-30 minutes. It's the same time investment as one round of proofreading. The difference is that proofreading catches errors; this pass catches invisibility. A post with no errors but poor citation structure is invisible to 40-60% of AI-generated answers on its topic. That's a meaningful miss, and it's fixable before you publish.

The deeper pattern these tests reveal

After running this pass on dozens of B2B SaaS posts, a pattern shows up clearly. The content that gets cited shares three structural properties:

  1. Discrete claimability. Each major section contains at least one sentence that stands alone as a fact or position. Not "this can be helpful" but "this reduced X by Y in our test."
  2. Named attribution anchors. The author's name, company, or a named methodology appears within the first 200 words and at least once per major section.
  3. Header-scannable conclusions. The post's argument is reconstructable from headers and bolded text alone without reading the body copy.

Posts that have all three get cited. Posts that have two of three get paraphrased. Posts that have one or fewer get passed over.

The good news: all three are writeable before you hit publish. None of them require technical changes. They're copy decisions, not engineering decisions.

What to do if your content fails all five tests

Don't unpublish it. Fix it in place.

The fastest wins are usually in the introduction (add one concrete benchmark or named claim) and the headers (rewrite topic headers as conclusion headers). Those two changes alone often move a post from "paraphrase background" to "cited source" territory.

If you want to know exactly which of your existing published posts are missing attribution anchors and how to fix them at scale, the LLMRadar Audit does that analysis across your full content library. We identify which posts have the highest citation potential and what specific rewrites would fix it. $197, one-time, results in 5 business days.

Run the LLMRadar Audit on your content library

Tomorrow: Why your product page is the hardest thing to get cited (and the three structural changes that fix it).

Internal links: Good Citation vs Bad Citation | Which Page Sections LLMs Cite Most in B2B | LLMRadar Audit

Author: Christine Johnson, Founder, OperatorIQ. Christine runs an autonomous agent studio that builds AI systems for small B2B teams. Her work on AI visibility and citation optimization has been cited in discussions of SAIO (Search AI Optimization) across B2B SaaS communities.