You may know pSEO as a growth marketing tactic used by companies like G2 and Zapier, which leveraged their vast proprietary data and structured content templates to generate thousands of pages targeting long-tail search queries. Now, recent advancements in AI are transforming pSEO from a niche tactic to a viable strategy for a broader spectrum of businesses. The evolution of pSEO can be broadly categorized into two phases:

• Version 1: The early iteration of pSEO relied heavily on template-based content generation. While effective, the rigidity of the content format limited its application.

• Version 2: The AI-enhanced iteration leverages advanced language models and machine learning algorithms to generate more diverse, contextually relevant, and high-quality editorial content at scale.

pSEO version 1:  Template-based content generation

In its initial iteration, pSEO was a specialized technique used primarily by engineer-driven growth teams at companies like Zapier, Pinterest, and G2. These teams created large numbers of nearly identical pages, changing only specific fields to make each page target specific long-tail search queries.

For example, Zapier generated thousands of app integration pages that looked virtually identical, with only the app names and integration details changing. Each page followed a template like this:

Title: Connect [App A] to [App B]

Content: Step-by-step instructions for connecting the two apps + other details on how the app integration improves business processes 

Call-to-action: Try [App A] + [App B] integration on Zapier

Using this templated approach and a database of app information, Zapier could automatically create unique pages for every possible app combination, such as "Connect Gmail to Slack" or "Salesforce integration with Trello." This approach allowed them to capture a wide range of precise user searches in a near-automatic way, significantly increasing their organic search visibility and traffic. 

Some notable examples of other companies that successfully leveraged pSEO version 1 strategies include:

1. Wise: Developed thousands of currency exchange pages using its financial database and a simple page template. Each page provides comprehensive information for specific currency exchanges such as “Transfer USD to EUR” and “Exchange GBP to AUD.”

2. G2: Created thousands of software comparison pages using a templated approach. Each page compares two or more specific software products within a particular category. Using a standardized page template and an extensive database of software information and user reviews, G2 automatically generated unique pages for thousands of software comparisons such as “Salesforce vs. Hubspot” or “Asana vs. Trello.”

3. Payscale created thousands of salary pages using a single template. Each page provides detailed salary information for specific job titles within various industries. Using a template and their database of salary information, Payscale generated unique content that satisfied thousands of long-tail searches, such as “software engineer salary in San Francisco” or “marketing manager salary at Amazon.” 

The rise of pSEO version 2: AI-Enhanced content creation improves quality and relevance

Over the past year or so, advancements in large language models (LLMs) have significantly advanced the power and potential of pSEO, enabling teams to move beyond the rigid, highly structured approach of pSEO version 1 and into more fluid, editorial-style content (pSEO version 2). Some of these LLM advancements include but are not limited to:

1. Improved natural language understanding and generation, allowing for more coherent and contextually relevant content creation.

2. Enhanced ability to maintain consistent tone and style across large volumes of content.

3. Better comprehension of nuanced topics, enabling the creation of more sophisticated and varied content.

4. Increased capacity to incorporate up-to-date information and adapt to changing contexts.

As LLMs continue to advance, the possibilities of pSEO are expanding exponentially. While pSEO version 2 maintains the core principle of capturing search traffic from a vast set of relevant long-tail queries through high-quality content variations at scale, its output is significantly more expansive. This evolution means that a broader range of companies can now leverage pSEO successfully to generate editorial-style content at scale. Businesses across various industries can create diverse content types, including:

• In-depth product comparisons  (e.g., Tome is doing this now with its AI tool comparison articles)

• Timely, editorial content  (e.g., Twingate uses AI to generate informative content about recent data breaches.)

• How to guides and tutorials (e.g., OpenArt uses AI to create “how to” content for generating different image assets) 

Leveraging pSEO version 2 now: Current limitations

pSEO version 2 is in full swing, allowing companies to generate high-quality, editorial-style content at scale. However, there are limitations regarding what Large Language Models (LLMs) can achieve now. Understanding these limitations is critical to building an effective pSEO engine that will circumvent these limitations to generate high-quality content.

Challenge 1: Limited knowledge scope

LLMs are limited to the information present in their training datasets. They cannot independently research or acquire new information, which means their knowledge could be outdated, incomplete, or biased. This limitation can affect the relevance and accuracy of the content they generate.

Solution: Leverage the right AI tools 

To mitigate the challenge of LLM’s limited knowledge scope, your team might consider combining LLMs with external knowledge databases or APIs that provide access to up-to-date information that the models themselves may not possess. This hybrid approach allows LLMs to pull in current data and facts to enhance the accuracy of the generated content.

• How daydream helps: daydream's technology enhances LLMs by integrating proprietary datasets and information extracted from diverse sources like PDFs, videos, and web pages not included in the model's initial training. This approach enables the generation of more comprehensive, up-to-date, and unique content that goes beyond the limitations of the LLM's core training data.

Challenge 2: Lack of contextual understanding

LLMs cannot decipher nuanced implications, analogies, or cultural references, leading to content that lacks deeper context and tonal awareness. This can result in content that feels generic or misses the subtleties required for certain topics.

Solution: Engage prompt engineering experts

One strategy to address the challenge of LLMs’ limited contextual awareness is to craft precise and detailed prompts that guide the models in generating more accurate and contextually relevant content.

• How daydream helps: Expert prompt engineers (like ours at daydream) can help you refine these prompts to specify the desired tone, style, and structure of the content and provide clear and comprehensive instructions on the information to be included. 

Challenge 3: Factual accuracy can waver

LLMs can sometimes generate plausible but factually inaccurate or misleading content, a phenomenon known as "hallucinations." This makes them unreliable for generating factual content without rigorous fact-checking and verification from authoritative sources. 

Solution: Develop a scalable QA process with subject matter experts

Incorporating reviews from subject matter experts (SMEs) adds another layer of factual reliability. SMEs can verify the accuracy of the content generated by LLMs and provide necessary corrections, ensuring that the final output is both accurate and high-quality.

• How daydream helps: At daydream, we incorporate content reviews from SMEs and/or content strategists to ensure content meets quality standards. 

Where things are headed

The future of pSEO looks promising as LLMs continue to evolve and become more intelligent. We anticipate that LLMs will improve in the following areas, making it possible for teams to create not just pSEO content but also traditional SEO content more efficiently.

1. Improved natural language understanding: Advancements in large language models (LLMs) are significantly enhancing their ability to understand nuanced language and context. This improvement is expected to lead to better alignment with user intent, thereby increasing the relevance and performance of content. For instance, newer models like GPT-4 have been trained on vast datasets, including Wikipedia, which helps them understand and generate more accurate and contextually appropriate responses.

2. Faster adaptation to trends: LLMs' ability to quickly process and synthesize large amounts of information will enable pSEO strategies to adapt more rapidly to emerging trends and changes in search behavior. For example, Google's Bard outperforms GPT-4 in handling queries related to recent data, showcasing the benefits of a federated learning approach, which allows for the pooling of data from various sources to stay updated with the latest information.

3. Better understanding of user intent: Enhanced reasoning capabilities of LLMs will lead to a deeper understanding of user intent, allowing for more targeted and effective SEO content strategies. Currently, companies are filling the gaps in LLM’s understanding of user intent by feeding LLMs data about user behaviors. 

Work with daydream

We’re working with some of the fastest-growing startups like Notion, Descript, and Tome to help them accelerate their organic growth through AI-driven pSEO content. Our platform provides all the tooling necessary to get a robust, high-performing pSEO engine up and running without the expensive price tag.

If you’re interested in using daydream and joining our growing list of customers, email us at hello@withdaydream.com to start the conversation.