Solution Smith's Open Transparent Modeling
To understand SEO, AEO, and make informed predictions about how content may perform in Google Search, it helps to have a basic mental model of how Google's search system works. Every experienced SEO relies on some version of a model when creating content, evaluating rankings, or recommending link-building strategies.
These models are not proprietary. They are built from publicly available information, observation, testing, patents, research papers, and years of practical experience. There is little reason for SEO agencies to treat them as trade secrets. Solution Smith believes transparency benefits both the SEO community and clients and openly shares the models used to explain and guide modern search optimization.
Google uses both the AI's native knowledge and retrieved documents filtered through traditional search
An AI model's native knowledge is learned during training and represented internally as mathematical relationships within a semantic vector space. This native knowledge is persistent, remaining available across queries. Confusing native knowledge with training data obscures how modern AI systems incorporate new information. They are not limited to the knowledge learned during their initial training. Training data teaches the model before deployment, while retrieved documents provide additional information after deployment. Information retrieved from external documents may eventually become part of the model's persistent native knowledge.
The model's native knowledge is its most efficient source for answering queries. When more current, specific, or authoritative information is needed, Google's search system retrieves relevant documents from its search index using Retrieval-Augmented Generation (RAG). In practice, these retrieved documents provide the fresh information that many people mistakenly believe comes from the model's training data. This retrieval process is computationally more expensive than using the model's native knowledge, increasing both energy consumption and operating costs.
AI engineers are developing technologies such as continuous pre-training, highly efficient sparse models (Mixture of Experts, or MoE), and structured memory layers to help models incorporate new knowledge more efficiently. The goal is to reduce dependence on large, multi-stage RAG pipelines by allowing more information to become part of the model's persistent native knowledge.
Long-Term Assessment
The hybrid AI and search model can be viewed as a transitional architecture. Although this is an informed assessment rather than an established fact, advances in AI technology and the economics of large-scale inference suggest that future systems may rely less on Retrieval-Augmented Generation (RAG). As models become more capable and efficient at retaining knowledge, the balance between persistent native knowledge and retrieved information may continue to shift.
Additional Consideration of Small Language Models (SLMs)
Beyond the technical architecture of hybrid AI and search, content creators, publishers, and governments are increasingly concerned with the accuracy of AI-generated answers and the licensing or compensation of information used by AI systems. These practical and legal considerations are likely to influence how future AI systems evolve.
One possible approach is the use of specialized Small Language Models (SLMs). Rather than relying solely on a Large Language Model (LLM) trained from web-scale data, an AI system could integrate licensed SLMs developed by trusted organizations. For example, a publisher such as The New York Times could develop and license an SLM containing its editorial knowledge. Integrating a trusted SLM may be more efficient and reliable than repeatedly retrieving and parsing HTML documents, while also providing clearer attribution, licensing, and compensation for the content creator.
Created
by Wayne Smith – Raising the Standards
Wayne Smith has worked in online marketing, search, and web development for several decades. His work includes building document retrieval systems, search engine simulations, and AI-assisted information systems. Drawing from software testing, information retrieval, and reverse engineering, he studies how AI systems discover, interpret, and synthesize information into answers.
This article is part of the State of AI series, which explores how AI systems process information and why those behaviors matter to businesses. Related topics, such as Answer Engine Optimization (AEO), apply those concepts to improving visibility within AI-generated answers.