There’s been a lot of talk online about generative AI systems replacing search engines, but the two actually work hand-in-hand. 

LLMs (large language models) and search algorithms share a symbiotic relationship, and they’re stronger together than apart.

Here’s what we mean. 

Search algorithms retrieve the world’s information, and LLMs interpret it. 

LLMs use search engines for factual grounding (i.e., like retrieving the most recent data), and search engines use LLMs for better query understanding. 

For this reason, it’s better to think of LLMs as an additional layer on top of traditional search instead of a replacement. 

If you want your content to perform well online, it needs to be readable and discoverable by search engines and LLMs. 

Since the two complement one another, optimizing for both yields the best results in terms of visibility, leads, and sales. 

Keep reading to learn more about how to leverage the special relationship between large language models and search algorithms.

How Do Traditional Search Algorithms Work?

First, let’s take a quick look at how search algorithms retrieve and rank online content. 

In particular, we’ll examine the two foundational systems that influence search algorithms more than anything else: PageRank and BM25. 

While Google’s current systems are far more sophisticated, they’re still built on the roots set by PageRank and BM25. You can think of them as the foundation, while machine learning, multimodal processing, contextual relevance, and entity understanding are layered on top. 

Also, each system solves a different problem. 

PageRank determines a page’s importance, while BM25 deals with textual relevance. 

Here’s a brief summary of how each works:

• PageRank is a system designed to evaluate the trustworthiness of websites through the quantity and quality of its backlinks. Essentially, it analyzes the web’s link graph and assigns authority scores to pages by treating each hyperlink as a form of endorsement. The idea is that if a domain has lots of backlinks coming from credible domains, it’s likely to be a reputable source.  
• BM25. Before there was semantic search, vector embeddings, or LLMs, BM25 was as good as it got in terms of relevance. It’s a keyword-based scoring algorithm based on term frequency, inverse document frequency (i.e., the term’s rarity across all documents), and document length. While BM25 set the gold standard, it’s a lexical keyword matcher that’s not capable of understanding the meaning behind or the relationships between words. 

These ranking systems were so important that they basically dictated the early SEO playbook, including tactics that are still used to this day. 

Without PageRank, link-building as a practice wouldn’t exist, and BM25 is the reason why marketers target and optimize for keywords. 

Modern use of classic algorithms

They’re also both still in use, although they’ve been given some modern bells and whistles. 

Elasticsearch, as well as many e-commerce platforms and academic IR systems, still use BM25 to help power their internal search systems. 

The strictly lexical system actually works great for exact-match keyword searches, but it begins to fall apart once the wording changes, like using the term ‘car’ instead of ‘automobile.’ 

Google still uses PageRank-style link analysis for its organic results, but it’s now one signal among many instead of being the star of the show. It works alongside things like semantic understanding, UX metrics, freshness, and spam detection. 

Backlinks are still extremely important ranking signals for organic search and AI discovery, it’s just that no single metric or link-based factor ‘takes the cake’ in terms of impacting rankings. 

That’s why marketers are moving away from static authority scores like DA and DR in favor of contextual relevance and editorial quality. 

How Do LLMs Work? How are They Changing the World of Online Discovery?

Before we explore how LLMs impact online search, let’s clear up a common misconception. 

All leading AI search models use transformer-based LLMs, like ChatGPT, which stands for generative pre-trained transformer. 

Without getting too technical, the term transformer refers to a general architecture class and not LLMs themselves.

Think about it this way: all GPTs are LLMs, but not all LLMs are GPTs. 

LLMs encompass other architectures like encoder-only (like Google’s BERT) and other variants. 

Perplexity, ChatGPT, Claude, and Gemini all use transformers as the ‘neural architecture.’ Transformers get their name because they transform one data sequence into another using a process called self-attention. 

Before transformers, models would read one word at a time from left to right as humans do. 

As a result, they struggled with long sequences because they would forget earlier tokens (words, subwords, etc.) in the sequence due to their short-term memory. 

This made it difficult to understand language because it’s full of long-range dependencies. 

To understand a sentence, you have to be able to ‘remember’ or ‘look back’ to earlier words and relate them to later ones. 

The self-attention process was revolutionary because it enabled AI models to process an entire data sequence at once instead of one token at a time. 

Since everything got processed simultaneously, it became possible for the model to understand meaning, context, and the relationships between words. 

What does this have to do with SEO?

Quite a lot, actually. 

Self-attention gives LLMs genuine semantic understanding, and that shift is what’s been reshaping how online content gets discovered and ranked. 

It’s why exact-match keywords matter a lot less than they used to.

It’s also why backlinks are evaluated through context and relevance rather than pure volume.

Also, AI assistants feel natural to use because they interpret language the same way we do, and they yield extremely accurate results. 

How Do LLMs and Search Algorithms Work Together Symbiotically?

LLMs are incredible for understanding language and generating original responses, but they’re incapable of updating themselves with new facts. 

This was a problem ChatGPT had early on, as before it implemented retrieval augmented generation (RAG), it was unable to provide answers based on anything besides its training data, which had a hard cutoff date. 

Through RAG, AI models like ChatGPT are able to retrieve fresh data and dynamic updates from, you guessed it, search engines. 

Search algorithms provide LLMs with current, verified, indexed information, which is exactly what they lack on their own. 

On the flip side, LLMs provide search engines with:

1. Enhanced query understanding 
2. Stronger interpretation of search intent 
3. Entity detection and understanding 

Also, Google has started to use large neural and language-model components in their ranking pipelines, such as using them to refine and re-rank search results based on relevance. 

In this way, LLM-based systems and search algorithms form a kind of feedback loop where one reinforces the effectiveness of the other. 

Therefore, if your content is optimized for both LLMs and search engines, you stand to yield the strongest results. 

What are the Best Ways to Optimize for LLMs and Search Algorithms?

Now that you know how both search systems work together, you can start designing content that appeals to both systems simultaneously. 

Essentially, we’ll show you how to develop a hybrid search strategy instead of adhering only to GSO or SEO. 

This type of campaign requires content that’s:

1. Crawlable 
2. Semantically rich 
3. Contextually relevant
4. Technically flawless 

Let’s take a closer look. 

Manage your reviews and reputation 

Whether you want to rank better in Google’s local pack or get more AI citations, improving your online reviews and reputation is absolutely essential. 

LLMs will check your reviews across multiple platforms, and they’ll see what users have to say about you on popular community forums. 

This means you need a solid review profile and a mostly positive brand sentiment to compete in the modern search landscape. 

Here are some tips:

• Learn how to respond to bad reviews in a prompt, professional manner 
• Don’t delete your negative reviews, as that will cause suspicion 
• Encourage your customers to leave positive reviews on Google and other platforms 
• Actively participate in Reddit threads and forums related to your brand 

Master entity optimization 

LLMs don’t match keywords; they identify entities and link concepts. 

Even search engines are beginning to incorporate entity recognition in their ranking systems, so you should focus more on entity optimization than strict keyword research. 

The goal is to get LLMs and algorithms to:

1. Recognize your brand as an entity in your area of expertise 
2. Trust your brand as an authority figure 

Here are some entity optimization basics. 

1. Ensure consistent naming across the web

Every instance of your brand name should be consistent, especially on your website. 

Inconsistencies (like “AMCO Inc.” vs. “AMCO Associated”) can lead AI systems to interpret your business as separate entities, which can throw off your entire strategy by diluting your authority. 

Besides naming consistency, ensure other facts are accurate as well, such as your NAP (name, address, and phone number). 

2. Identify your key nodes

Map out the core nodes of your business: key people, products, services, concepts, and ideas. 

Make sure these entities appear across major knowledge sources like Wikipedia, Wikidata, your website, and social media profiles.

You should also develop detailed author biographies and link them to their respective websites and social profiles. Author credibility is a big deal to LLMs and search algorithms, so don’t be shy about showing off your team’s expertise and accolades. 

3. Build content around topical clusters

Rather than optimizing individual keywords in isolation, create content that fleshes out your brand’s full topical universe. 

Group related topics, entities, and subtopics into comprehensive clusters to provide context and depth. This approach helps both search engines and AI systems understand the relationships between your brand’s entities.

Don’t forget technical SEO 

Organic SEO and GSO both require flawless technical elements, like structured data, site speed, and crawlability. 

Semantic HTML and schema markup are essential for:

• Making your content easy to parse and cite for LLMs 
• Qualifying for Rich Snippets on Google 
• Disambiguating aspects of your content for increased accuracy 

Check out our ultimate technical SEO guide to learn more. 

Digital PR is your best friend 

High-quality, contextual brand mentions and backlinks are strong needle-movers for organic search and AI discovery, and they’re what digital PR does best. 

Digital PR techniques include:

• Networking with journalists on platforms like HARO and Qwoted 
• Securing media mentions on relevant websites 
• Publishing thought leader content, original research, and expert interviews 

In short, digital PR creates the exact type of buzz you need for your brand to rank higher and get cited more often. 

Concluding Thoughts: The Symbiosis Between Search Algorithms and LLMs 

It’s time to stop thinking of search algorithms and LLMs as competing forces and start realizing all the ways they work together. 

By taking a hybrid search approach, you can improve your GSO and SEO without launching separate strategies. 

Do you want to achieve the best of both worlds for your brand?

Check out HOTH X, our fully managed search optimization service!