The Rebirth of PR as a Retrieval Source for LLM Visibility

Large language models (LLMs) are reshaping how people access information. LLMs show users the information that AI crawlers have indexed that meets their request. PR professionals – and anybody who works in brand management – who manage public communications must respond to this shift in how users learn about brands and how AI shares this information. LLMs have changed PR for everyone.

Traditional media is no longer the only or primary gateway to audience attention. This article explores how PR is changing and how to capitalize on the shifts to share your brand’s message.

The New Reality of PR

For decades, PR success meant landing coverage in prestigious publications. The formula was straightforward: craft compelling stories, build relationships with journalists, and measure success through media impressions, share of voice, and article placement. 

LLMs are emerging as primary tools for audience engagement. This shift requires PR practitioners to develop a nuanced understanding of how these models retrieve, process, and present information about brands. This is not to say that traditional media is becoming obsolete, but rather that people will go to AI when they want a quick summary of top news.

When customers ask AI chatbots about your brand, they receive a synthesized response that draws from various sources across the internet. The narrative AI constructs about your company may never reference a specific media outlet. Yet, this summary shapes brand perception as powerfully as any front-page feature.

LLMs prioritize context-rich, nuanced storytelling, reshaping communication dynamics between companies and their audiences. The result is a new paradigm where brand narratives must serve both human readers and algorithms.

When LLMs misrepresent your brand or amplify negative sentiment, the impact ripples across every customer interaction with AI. PR News Online notes that these models analyze millions of conversations across social media, news articles, and review sites to detect sentiment changes, making comprehensive reputation management more critical than ever.

LLMs as PR's New Front Page

Unlike traditional media coverage, which reaches readers who consume specific publications, LLM-generated responses reach users at their moment of curiosity. This creates both opportunity and risk for brands. Every query becomes a chance to shape perception and a potential vulnerability if the narrative gets out of your control.

Unlike Google, which relies on keyword-based ranking, LLMs prioritize conversational context. This changes how visibility works. Rather than optimizing for specific search terms, PR professionals must now ensure their brand narratives provide meaningful context that LLMs can integrate into conversation flows.

This difference manifests in how users interact with these systems. Consider how a potential customer might ask about your industry:

The LLM query demands contextual understanding of which companies exist and how they're perceived regarding specific values and practices. Without a contextual framework around your brand, you won't appear in these conversations.

What makes this challenging is how LLMs decide which information to prioritize. They evaluate content based on its usefulness within specific conversational contexts. They emphasize positive sentiment and accurate representation in their retrieval mechanics, often filtering out extreme viewpoints in favor of balanced perspectives. This is important because AI outputs are already synthesized, which can obscure individual perspectives and make it harder to grasp the spectrum of perspectives.

Traditional journalist outreach must now be supplemented by strategies tailored for LLM visibility. This means creating assets that serve dual purposes: compelling human readers while providing clear, structured information for AI.

The strongest influences on LLM retrievals are consistent messaging across platforms, structured data in press releases, and a widespread digital presence. When multiple sources align in their representation of your brand, LLMs are more likely to reflect that consensus in their responses.

While traditional approaches often emphasized creating waves of attention through big announcements, LLM visibility rewards consistent, aligned messaging across a broad digital footprint. The goal becomes creating a coherent brand narrative ecosystem that AI can synthesize.

Optimizing Press Releases for LLMs

Press releases must evolve to meet new requirements that increase the likelihood of their visibility on LLMs. Structured formats with clear metadata enhance visibility in LLM outputs. This means press releases should feature explicit organization with descriptive headlines, labeled sections, and factual statements that can be extracted and repurposed.

Consider this structural approach:

• Use descriptive headlines that state the core news update directly
• Include a clear summary paragraph with key facts in the first 100 words
• Organize additional content with descriptive subheadings
• Incorporate factual statements in simple, declarative sentences
• Include relevant dates, locations, and quantifiable impacts
• Add structured data when possible (e.g., event details in a consistent format)

Language patterns also influence citation probability and sentiment analysis. LLM algorithms, which are trained on conversations and pick up these patterns, favor conversational tones and context-rich narratives. Press releases must integrate contextual cues to improve retrieval and sentiment accuracy.

Effective language strategies include:

• Using natural, conversational language rather than corporate jargon
• Providing explicit context around industry-specific terms
• Including direct quotes that express brand values and perspective
• Creating contrast between positive developments and challenges
• Avoiding promotional language that LLMs might filter out

Distribution strategy matters as much as content creation. Traditional newswire services remain valuable, but they're one component of a comprehensive approach. PR professionals should ensure brand communications appear across diverse platforms, creating the consensus signals LLMs interpret as credibility indicators.

These platforms include traditional media outlets and industry databases, academic repositories, verified social media accounts, and official company channels. The goal is to create a network of consistent information that LLMs can draw upon when responding to queries about your brand or industry.

PR for a Human and Non-Human Audience

Creating content that serves both human journalists and AI requires understanding their different needs. Journalists who work in traditional media relations focus on narrative hooks, exclusivity, and relationship building. LLM optimization, by contrast, requires consistency, clarity, and structured information.

The dual focus on human readers and machine learning systems doesn't mean creating separate assets, but rather enhancing existing materials to serve both audiences.

A practical example of this approach would be a product launch announcement that opens with structured factual information (product name, launch date, key features, pricing) before transitioning to the narrative elements that engage human readers. This structure ensures that LLMs can extract the essential information while providing journalists with storytelling components.

LLMs draw from a wider pool of sources than most journalists. While media placements rely on official statements and trusted sources, LLMs incorporate everything from social media conversations to review sites and forum discussions. This makes comprehensive reputation management across all platforms essential.

This environment requires collaboration where AI tools complement human creativity by automating repetitive tasks while providing strategic insights about messaging effectiveness.

This collaboration extends to content personalization. LLMs can tailor messages based on individual preferences, boosting engagement rates. They can also craft platform-specific content, enhancing visibility and user interaction across channels. This capability allows PR teams to develop dynamic storytelling approaches that resonate with target audiences while maintaining the factual consistency that LLMs require.

Tracking LLM Retrievals

Measuring PR success in the LLM era requires new approaches to tracking and analytics. Unlike media monitoring, which focuses on publication placement and reach, LLM retrieval tracking examines how AI incorporates brand information into responses.

PR firms can track LLM retrievals through query testing, sentiment analysis, and source attribution monitoring. This involves querying major LLMs with brand-relevant questions and analyzing the responses for accuracy, sentiment, and information sources.

A practical approach to LLM monitoring includes:

1. Establishing how your brand currently appears in responses to industry and direct brand queries
2. Comparing your LLM representation against key competitors
3. Monitoring the emotional tone associated with your brand across different query types
4. Identifying which sources LLMs cite when discussing your brand
5. Observing how brand narratives change over time in response to new information

AI campaign measurement tools now provide precise insights into audience engagement and sentiment trends. These platforms also offer competitive benchmarking, allowing brands to compare their LLM representation against industry peers.

Goodie is a powerful platform for brands to monitor and optimize their presence across AI chatbots like ChatGPT and Gemini. The platform offers precise insights into audience engagement, sentiment trends, and competitive benchmarking, allowing brands to compare their LLM representation against industry peers. With real-time visibility scores and optimization tools, Goodie helps brands shape and enhance their AI representation. 

The practical implementation of these tracking systems might involve creating a query calendar that tests different aspects of brand representation across major LLMs. For example, a comprehensive monitoring program might include:

• Weekly testing of direct brand queries 
• Bi-weekly testing of industry landscape queries 
• Monthly testing of value-specific queries 
• Quarterly deep-dive analysis of source attribution patterns

Key performance indicators for LLM reputation management include:

• Retrieval rate for brand-specific queries
• Sentiment in AI responses
• Accuracy of product and service descriptions
• Competitive share of voice in category-level queries

PR Agency Transformation

As LLMs reshape information discovery, PR agencies are evolving to meet new client needs. Many agencies are developing proprietary tools to optimize brand narratives for LLM retrievals.

New service offerings include:

• LLM visibility audits that assess current brand representation
• Narrative consistency programs to ensure unified messaging across platforms
• Crisis simulation that tests how negative narratives might propagate through AI 
• Press releases optimized for distribution
• Ongoing LLM retrieval monitoring and competitive analysis

Developing these capabilities requires significant investment in technology and expertise. Agencies need tools to monitor LLM outputs at scale, analysts who understand the nuances of retrieval mechanics, and strategists who can translate technical insights into actionable communication plans.

For example, real-time feedback from LLM retrievals might reveal that certain messaging elements are overlooked or misinterpreted. This insight can trigger immediate adjustments to press release language or distribution strategy rather than waiting for traditional media monitoring reports.

Mastering LLM visibility provides agencies with strategic advantages over traditional approaches. Those who understand and influence AI narratives can ensure clients maintain control over their brand story regardless of how customers access information.

Action Plan for Brand Reputation with LLMs

Organizations of all sizes, from global corporations to nonprofits and individuals with personal brands, need to understand how they’re represented in LLM outputs. Start by conducting query testing across major LLMs using questions your ICPs might ask about your brand, products, or industry. Document discrepancies between your desired brand narrative and how LLMs portray you. 

Implement basic monitoring to track how your brand appears in LLM responses over time. Platforms such as Goodie AI can help you track brand sentiment, visibility, and other metrics to have more control over how AI represents your brand. Taking the AEO search assessment is a great way to understand your brand on AI platforms.

The brand narratives appearing in LLM responses influence how all stakeholders perceive your organization. Organizations that shape AI perceptions proactively gain a competitive advantage in managing their public relations in this new environment.