About Recent Data Bits
Solution Smith’s Recent Data Bits is a running log of timely information focused primarily on marketing and SEO, published by Solution Smith. In the broader sense, it explores multi-channel marketing and the tools, methods, and observations involved in building and maintaining an online presence.
An RSS feed is available for Solution Smith’s Recent Data Bits, allowing users to subscribe with an RSS reader and receive updates whenever new entries are added. Entries may link to guides based on practical experience and tested information, and may occasionally discuss events affecting the online industry.
Published 4-28-26
Evergreen Content and AI / LLM Answer Engines
Evergreen content is a long-term SEO strategy of evergreen SEO, focused on publishing material that remains consistent and relevant over time. This type of content helps build authority and trust, often attracting backlinks and establishing a reliable baseline of credibility for a site. It also has ongoing value in LLM answer engines, which tend to favor stable, consistently accurate information in their training and responses.
Published 4-26-26
Evergreen content is a long-term SEO strategy of evergreen SEO, focused on publishing material that remains consistent and relevant over time. This type of content helps build authority and trust, often attracting backlinks and establishing a reliable baseline of credibility for a site. It also has ongoing value in LLM answer engines, which tend to favor stable, consistently accurate information in their training and responses.
Published 04-16-26
Purchase Here: AI Answers Exit-Path Strategy
When a broad query such as “what is the cost of office door glass” is entered, AI systems generate fan-out queries to gather relevant information. If the query is ambiguous or allows for multiple interpretations, the response is often organized into categories that reflect those differences.
The AI session does not end with the initial question. As the response expands, users are exposed to multiple options and can drill down into their specific needs. At this stage, product comparisons and related details are more likely to be surfaced and cited by the AI—creating an early opportunity to build awareness and position specific products or brands.
Once immediate intent is satisfied, users often move on to related decisions—or exit the AI session by clicking a citation or searching for a specific brand. When a user searches for a brand mentioned by the AI, it reinforces that entity’s relevance, increasing the likelihood of future mentions and citations.
By aligning content with both the primary query and its likely fan-out queries, marketers can influence downstream mentions, citations, and ultimately user decisions beyond the first answer.
In AI systems, visibility is no longer limited to rankings. Instead, it comes from being included directly within the generated response through mentions and citations.
The Optimizing Entity Visibility for AI and LLMs page explains how content maps to AI-recognized entities. The Full Stack SEO AI Chat, an example of an AI brand ambassador, demonstrates how these mentions and citations can be generated in practice.
Published 04-14-26
Digital Presence Optimization is built on a search everywhere mindset. It moves beyond drive-by profile creation, which produces little value without engagement or direct interaction with the target audience. Visibility is not created by the presence of a link alone, but by the activity and recognition that occur around it.
While many SEO purists consider profile links to be pointless due to noindex attributes or limited internal linking, this view ignores their actual function. When supported by real participation, profiles become points of contact and sources of recognition within a community. They enable interaction, build familiarity, and generate mentions that extend beyond the platform itself.
In this context, the technical status of a link becomes less important than the activity it supports. A noindex profile can still contribute to SEO visibility because the engagement it generates creates signals that exist outside of the page itself. Search engines do not rely solely on what they crawl ... they observe signals created across systems and respond to broader patterns of engagement and reach those links help create.
This mindset also aligns with large language models and AI-driven search systems, which rely on consistent signals across multiple sources to understand entities and determine relevance. Visibility is reinforced when a brand is recognized across platforms, not just within a single indexed page. Search engines do not create authority ... they detect it across systems.
Published 04-09-26
LLMs don't read raw keywords; they read attributes ... technically tokens.
It is well known that LLMs build a network of concepts around these vectorized tokens, and that semantic triplets created by NLP, Natural Language Processing, serve as the basic interface to provide this tokenized and vectorized data into AI systems.
Consider the keyword #234#, without the NLP context, it is unseen to LLMs but provide the context ... The toner count reset code for brand-x toner cartridge is #234#. Press the setup key and enter the code on using the numeric pad ... with the context, the LLM sees the code as a datapoint, and the answer to the question “How do I reset the toner count for brand-x toner cartridge”.
The data within a lorebook or AI Brand Ambassador does not specifically require this long form human language pattern. The pattern used by dictionaries often replaces the “is” with a colon. So the following pattern works.
Published 04-08-26
Tenured SEO is not dead. Modern AI-aware SEO builds on its foundations rather than replacing them. While some outdated tactics and strategies no longer produce results, the core principles of tenured SEO persist because they reflect how information retrieval systems identify relevant, trustworthy content. Despite frequent claims that “SEO is dead,” what has actually changed is how these systems evaluate and prioritize signals.
Keyword-based SEO still exists at a foundational level, but keywords can evolve into entities as search systems gain more context. Likewise, keyword density still plays a role, but it is now complemented by deeper semantic structures, such as entity relationships and contextual associations.
What has changed is not the foundation, but how relevance and trust are evaluated. As search systems evolve, so do the signals used to determine what content deserves visibility—making this an ongoing and often debated topic within the SEO community.
Published 03-31-26
Keyword Density in Modern SEO: From Query Prediction to Semantic Matching
Keyword density, or keyword frequency, has long been a fundamental part of SEO and document retrieval systems—and for good reason. The rise of large language models (LLMs) does not eliminate keyword density; instead, LLMs reinforce keyword density's role as a tenured element of SEO, because LLMs need unambigous content structure, which may not be possible with pronouns.
Keyword usage signals what a document is about and enables prediction of which queries it can match. Rather than a fixed value, keyword density operates as a range that varies by topic, intent, and available semantic variation.