How to Audit PR Content for LLM Search

Custom GPT

As generative AI systems become the default discovery layer for information, brand visibility is no longer about search ranking — it’s about retrievability, trust, and citation within LLMs.
Your next audience isn’t just human readers or journalists. It’s language models.

Today, most PR content is still optimized for Google’s crawler logic — keywords, backlinks, and domain authority. But large language models like ChatGPT, Perplexity, Gemini, and Claude use different heuristics. They look for semantic clarity, factual corroboration, and domain trust.
That means the content your PR team publishes can either be weighted as evidence — or filtered out entirely before the model even reads it.

To bridge this gap, we built the AI Visibility Evaluator — a Custom GPT designed to score and optimize PR, brand, and editorial content for visibility within AI ecosystems.

Why LLM Visibility Now Matters

When an LLM retrieves information from the web, it doesn’t simply index links. It goes through a multi-stage pipeline:

  1. Retrieval: Fetching the most semantically relevant snippets.

  2. Filtering: Removing low-quality or duplicate sources.

  3. Contextual Scoring: Weighing passages based on how directly they answer a question.

  4. Attribution: Selecting which sources to cite or paraphrase.

  5. Trust Adjustment: Re-ranking content using knowledge graph validation, domain reputation, and recency.

Each of these layers uses different signals than traditional SEO — meaning PR content must now be engineered for LLM relevance, not just visibility in search.

What the AI Visibility Evaluator Does

The AI Visibility Evaluator scores and explains how any piece of content — from a press release to a thought-leadership article — performs against LLM visibility criteria.

It evaluates across five key pillars:

The model delivers a 100-point AI Visibility Score, with granular notes, inline annotations, and concrete actions such as:

  • “Add a citation to an independent authority to increase trust weighting.”

  • “Rephrase this paragraph into declarative statements for contextual relevance.”

  • “Add a timestamp and FAQ block to improve retrievability.”

How It Works

When a user uploads or pastes a PR draft, the Evaluator performs a structured audit:

  1. Semantic & Structural Scan: Checks for natural phrasing, headings, summaries, and entities.

  2. Trust Validation: Detects citations, domain indicators, and authorship transparency.

  3. Relevance Scoring: Measures factual clarity, corroboration, and citation strength.

  4. Penalty Detection: Flags promotional tone, duplication, or missing metadata.

  5. Report Generation: Produces a table and executive summary — complete with “Weakest Passages → Suggested Rewrites.”

The result isn’t just a score — it’s a visibility blueprint that shows how to make content discoverable and trustworthy in the LLM layer of the web.

Example Use Cases

For PR Agencies

  • Evaluate releases before distribution to ensure they survive filtering and are retrievable by AI-powered assistants.

  • Compare multiple drafts and identify which version is more likely to be cited by LLMs.

For In-House Communications Teams

  • Audit evergreen web pages, brand explainers, and thought leadership for semantic clarity and factual depth.

  • Track whether content aligns with existing knowledge graphs and authoritative domains.

For Enterprise Marketing & SEO Teams

  • Shift from keyword-centric SEO to LLM-centric visibility engineering.

  • Use scoring data to train editorial teams in writing for retrieval and citation weight.

Four Core Capabilities

The AI Visibility Evaluator isn’t just a scoring tool — it’s an LLM literacy framework for communications teams.
It supports:

  1. Content Scoring: Full audit with 100-point breakdown and detailed rationale.

  2. Comparative Benchmarking: Rank multiple drafts or press releases by AI visibility.

  3. Optimization Guidance: Targeted rewrite suggestions grounded in LLM logic.

  4. Entity and Trust Validation: Detects missing citations, authorship, or domain credibility issues.

Example conversation starters include:

  • “Score this press release for AI visibility and give me fixes.”

  • “Compare these two PR drafts and tell me which to publish.”

  • “Audit this article for entity precision, citations, and chunk structure.”

  • “Rewrite weak sections to maximize LLM retrievability.”

Why This Matters for Communications Leaders

The public internet is becoming an AI-mediated knowledge graph.
LLMs don’t read your press releases the way humans do — they evaluate them probabilistically, favoring clarity, corroboration, and trust.

If your brand’s content can’t be retrieved, trusted, or cited, it’s invisible — no matter how strong the SEO metrics look.

The AI Visibility Evaluator helps PR and communications teams evolve from writing for algorithms to writing for language models — ensuring every piece of content is discoverable, citable, and persistent across the next generation of AI interfaces.

Next Steps

Organizations can deploy this Custom GPT internally to audit PR output, train content teams, or benchmark agencies on LLM visibility performance.
It turns abstract AI reasoning into a measurable, repeatable quality standard — one rooted in how models actually interpret and trust the web.

AI Visibility, PR, Custom GPTFrancesca Tabor