The AI Search Revolution: How Large Language Models Are Redefining Digital Discovery

Key Takeaways

• Get into the AI model’s source codes (core knowledge)
• Check regularly what they already know about your brand (and your competition)
• Educate models about your brand (upload content, create custom GPTs)
• Leverage AI-driven search features by optimizing your existing content for both LLMs and humans

What to Know About AI-Driven Search

When it comes to AI discovery, nobody is an expert yet, but I have interesting findings that I would like to share. Here, I will walk you through the AI-driven search, how it’s changing the search landscape, how it’s different from traditional SEO, and look into the real-life case study – providing deep insights on what exactly we did, and the results we achieved. There’s a lot of excitement and, let’s be honest, a bit of uncertainty surrounding AI right now. 

Many B2B projects are facing challenges, including decreased organic traffic and the impact of recent Google updates like the Helpful Content Update. For example, organic traffic is down between 15% to 30% YoY or even QoQ. 

Clients are expressing frustration that simply ranking number one on Google isn’t enough anymore. The reality is, search has changed. AI is becoming ubiquitous, and traditional B2B SEO is no longer as effective.

The key concern? How do we continue growing traffic and leads in this new environment? Failing to optimize for AI risks losing reach, especially as platforms like GPT and Perplexity gain market share. Many are unclear on how to integrate AI into their marketing mix, leading to questions about what to do and why.

AI and LLMs vs. Traditional Search

Let’s address the core question: How do AI and LLMs differ from traditional search, and what steps can you take now as a B2B decision maker?

Traditional search starts with intent formulated as a keyword, aiming to provide a ranked list of relevant pages. LLMs, on the other hand, predict and generate the best next word in response to a query, operating as well-trained predictive machines.

Key differences include:

• Query handling: Traditional search is keyword-based, while LLMs consider context, intent, and broader correlations.
• Information retrieval: Traditional search retrieves indexed content, while LLM-driven search involves parsed content and synthesized answers.
• Result presentation: Traditional search provides a list of links, while LLMs offer direct conversational responses.
• Personalization: Traditional personalization relies on cookies and account history, while LLM personalization remembers conversation context.
• Knowledge Updates: LLMs initially had a knowledge cutoff, but now they have search functions and can learn about the real-time world. Traditional search always provides up-to-date information, provided that’s what ranks the highest.
• User Interaction: Traditional search involves short queries, while LLM interaction is dialog-based.

How to be LLM-Friendly: Key Principles

Despite the complexity, LLMs are still machines with algorithms, meaning they can be optimized. This is actually the good news – we’re dealing with machines that follow certain patterns, which means we can optimize for them. 

Step 1. Get Inside the Source Code

LLMs are trained on massive datasets like Common Crawl, which serves as a foundation for their knowledge. They learn about the world from pre-training data, including those from around the web. Think of this as the “early days of Google” equivalent when you had to be registered in certain directories to be found.

How to check what Common crawl knows about your business:

• Check if your brand is indexed by Common Crawl using their index checker tool
• Look at the most recent monthly crawl data (they update literally every month!)
• If you’re not there, this is your first priority – you need to be in that data set so machines know about your brand
• This is how you get right to the core, educating the LLMs about your business from the training stage itself

Step 2. See What Models Already Know About You

It’s very interesting to understand what LLMs already know about your business – or what they don’t know!

What you can do right now:

• Use HubSpot’s AI Search Grader to check what GPT, Perplexity, and Gemini know about your brand
• The tool shows not just if they know you exist, but what information they have
• Check what they know about your competitors too (this is an interesting angle!)
• Identify gaps where LLMs have limited understanding of your business

Step 3. Educate the Models About Your Brand

One of the most fascinating things I’ve discovered is that you can actually educate these models yourself through direct interaction.

What you can do right now:

• Build custom GPTs with your knowledge base (this is what I did, and now it remembers details about my business that aren’t even on my website!)
• Upload content about your business to platforms that feed into LLM knowledge
• Regularly interact with AI platforms to provide accurate information about your services
• The more you educate the platform about your brand, the more it knows – it does remember!

Step 4. Structure Your Content for LLM Comprehension

LLMs are still machines – they read your content and tokenize it, transforming it into numeric streams they can process.

What you can do right now:

• Use logical structure to make it easier for LLMs to process your content
• Double down on definitions, examples, and detailed descriptions
• Create content as if it was a ready-to-go answer in ChatGPT
• Work with your content to make the job of LLM models easier
• Remember: when you double down on these elements, you’ll appeal to both LLMs and humans

Step 5. Rethink Your Reference Strategy

LLMs rely heavily on references, but differently than traditional SEO approaches.

What you can do right now:

• Start referencing Reddit and Quora discussions with large followings
• Incorporate credible statistics and educational articles
• Change how you reference stuff compared to traditional methods
• This transformation of your content seems to improve how LLMs understand it

Step 6. Optimize Images with Context

If you’ve ever generated an image with AI tools, you’ll notice the file names are extremely long and descriptive – that’s a clue!

What you can do right now:

• Instead of short alt tags, provide more contextual descriptions
• LLMs can technically “see” images but rely more on words and context
• Give LLMs better context for what these images represent
• Think sentence-level descriptions rather than keyword phrases

Step 7. Implement Schema Markups

As they are machines, LLMs parse content similar to how Google does – so schema still matters!

What you can do right now:

• Add FAQs and schema markup to make it easier for LLMs to understand your content
• Implement HowTo articles and other schema.org structures
• Remember, it’s still a machine – when you give it structured data, it performs better

When you add all these elements together with the best SEO practices, this is where the results come in. We’ve seen this work incredibly fast – SEO has been put on steroids since 2022, and now with AI overviews, you can get even faster results. The game has changed, and these strategies will help you stay ahead!

The Blended Reality: Google and AI Overviews

Google has integrated AI overviews into its search results, appearing in more and more significant portions of searches. This represents a blend of traditional search and LLM-driven search. AI search is measurable, and tools like Ahrefs and Semrush can track which keywords and pages appear in AI overviews.

Case Study: Generative Engine Optimization (GEO)

We’ve seen great success with the Generative Engine Optimization (GEO) framework. In one case study with Case IQ, a case management platform, we focused on user intent, structured content, definitions, examples, detailed descriptions, explanations, links to glossaries, reviews, social proof, and referencing. 

The results? This led to significant growth in keywords appearing in AI overviews in just one month, positively impacting impressions and targeted traffic. Below are the key results in only 2 months:

• +26% new keyword rankings in top 3 positions
• +33% increase in AI Overviews and featured snippets
• +426% more keywords in AI overviews specifically

Most interestingly, while measuring the results, we also noticed that the search volume for the keywords whose searches have AI Overviews increased from 3,330 to 16,750 (403%) in the same period.

Key Principles of GEO:

• Prioritizing user intent over keywords.
• Structuring content logically for LLMs.
• Providing definitions, examples, and detailed descriptions.
• Referencing reviews, social proof, and relevant discussions.
• Optimizing images with contextual descriptions.
• Adding schemas, FAQs, and how-to articles.