What's actually possible with LLM search optimization?

I've been diving deep into how Large Language Models (LLMs) are transforming digital marketing, and what I've discovered is fascinating. These AI systems are rapidly becoming the new gatekeepers of information, fundamentally changing how consumers find and evaluate products. Through my research, I've uncovered emerging patterns in how LLMs source information and what this means for our content strategies.

How LLMs collect data

In my investigation, I've found that LLMs currently gather information in two primary ways:

Pre-trained dataset retrieval: The LLM pulls information from its training data, which might be months old. From my latest findings, this data gets refreshed approximately every 3-6 months.
Real-time search integration: The LLM actively performs search queries using external APIs. I've noticed different models use different systems - some leverage dedicated search APIs, while others tap into existing search engines.

What's interesting is that both approaches follow similar patterns for collecting data, but the time lag with the first option is significantly greater.

What I've learned about LLM optimization

Through my experiments, I've identified several crucial factors that shape how LLMs decide what to recommend:

Limited source selection: For cost efficiency reasons, I've observed that LLMs typically only draw from 5-10 websites when responding to queries. This creates an incredibly competitive landscape where visibility in these limited sources becomes critical.
Brand site inclusion: I've consistently seen that LLMs frequently include a brand's own website among their sources. Based on these patterns, I believe the optimal strategy is approximately 50% focused on site content growth/optimization and 50% on external content targeting.

My case studies: How LLMs choose sources

I've conducted several experiments to understand how LLM responses are influenced by their source prioritization. Here's what I've discovered:

Case study 1: "What is the best probiotic?"

[Note: Image would be placed here showing how different LLMs prioritize sources]

In my testing, I found that when Company A appeared as the primary source for this query, it was recommended first by the LLM. When it appeared as a secondary source, it was mentioned second. I've seen this pattern consistently across multiple tests, confirming the critical importance of source prioritization in LLM recommendations.

Case study 2: "Best stores for curtains"

[Note: Image would be placed here showing source prioritization for home decor queries]

My research uncovered that about twenty specific interior design blogs consistently serve as trusted sources for LLMs on home decor topics. When articles on these sites closely match user prompts, they dramatically influence recommendations. I observed this pattern across multiple LLMs, with and without web search functionality enabled. I'm even planning to test adding a fictional store to one of these articles to see if LLMs will begin recommending it!

Case study 3: "Best site for German vacation homes"

[Note: Image would be placed here showing long-tail query results]

This was my most eye-opening discovery. Company B was consistently recommended simply because one particular travel blog had written an article listing the best sites for German vacation rentals, with Company B mentioned at the top. According to my research using Ahrefs, this is a query that gets regular search volume. It's fascinating evidence of how being prominently featured in the few articles that address specific long-tail queries can dominate LLM recommendations.

Strategic implications I've identified

Based on these findings, I'm developing a comprehensive approach to LLM optimization that includes:

Prompt identification: First, we need to identify which prompts and queries are most relevant to our business and worth targeting. I'm currently working on methods to track and categorize these effectively.
Source analysis: Next, we need to determine which websites currently serve as primary sources for these prompts in LLM responses. This is challenging but crucial work.
External content placement: My experiments suggest that position within an article and surrounding context play major roles in LLM citation likelihood. We need to develop strategies to be featured prominently in high-priority external articles.
Long-tail opportunity exploration: I'm particularly excited about untapped long-tail prompts where creating targeted content on receptive platforms could establish a brand as the go-to recommendation with relatively little competition.

Leveraging existing assets

I've discovered that companies with established industry resource sites have a significant advantage in this new landscape. During my research today, I found that certain industry-specific information portals frequently serve as primary non-brand sources for LLMs in their respective niches.

For example, Company C has an industry portal that was consistently used as a trusted, independent source for supplement-related queries across multiple LLMs. When you filter out all the brand websites, this portal was often the first neutral source an LLM would reference. This presents an incredible opportunity to optimize content on affiliated platforms to improve visibility in LLM responses.

Where do we go from here?

I can't emphasize enough how experimental this field is right now. We're all learning as we go. But I strongly believe that the investment required today to establish favorable positioning in LLM responses may be just a fraction of what it will cost in the future as competition intensifies.

Here's what I recommend we do next:

Start tracking specific prompts relevant to our products and monitoring which sources LLMs prioritize for those queries
Identify the most influential external content platforms in our space that LLMs consistently reference
Develop relationships with these platforms to gain prominent mentions in their content
Strengthen our own site content with LLM optimization in mind
Look for underserved long-tail opportunities where minimal investment could yield significant LLM visibility

The companies that start experimenting now will have a tremendous head start as LLM search becomes the norm. I'm excited to continue exploring these strategies and sharing what I learn along the way.

The insights in this article represent my emerging research in a rapidly evolving field. I recommend testing these strategies and refining them based on your specific business context and objectives.