LLMs vs. Traditional Search: What’s Changing in SEO + How to Adapt

Search engines are systems that retrieve information, while LLMs are a type of artificial intelligence that generates answers. LLMs aren’t replacing search results, but they’re shifting the way people find information — and how digital marketers approach SEO.

With the rise of generative AI and AI search, LLMs are officially shaking up the world of search engines.

And while the two systems serve different purposes (to generate an answer vs. retrieve the information), it feels like LLMs are replacing search engines — when they should actually be working in tandem.

LLMs can synthesize information into easily understandable content. Search engines crawl, index, and present content that is the most valuable.

Through LLM-powered search engine integrations, like Google’s AI Overview, the two are merging. Which means search engine optimization needs to adapt.

HawkSEM SEO Manager Samantha Ridgway walks us through the key differences between LLMs and traditional search results — and how to make sure your content appears for both.

Traditional search results are the list of blue clickable links that search engines display in response to a query. (Image: Adobe)

What are LLMs?

LLMS (large language models) are AI systems that generate natural language answers based on a vast amount of training data. Tools like OpenAI’s ChatGPT, Google’s Gemini, and Perplexity can synthesize information, interpret user intent, and provide context-aware answers.

Unlike traditional search engines, LLMs allow for follow-up questions and a back-and-forth conversational experience — allowing users to refine, question, and explore topics interactively.

What are traditional search results?

Traditional search results are the list of blue clickable links that search engines (like Google and Bing) display in response to a query. The results are ranked based on relevance, quality, and credibility to help users find the best possible answer.

Google search and other search platforms’ crawlers used to leverage keyword-based algorithms to determine which results were most relevant to the user; however, search results now try to match user intent.

LLMs vs. Search results: Key differences

LLMs generate information, while search engine results retrieve information.

“Traditional search engines surface links and show you where to look. LLMs, on the other hand, synthesize information and give you the answer directly,” says Ridgway.

“Search relies on crawling and indexing billions of pages, while LLMs draw from trained knowledge, context, and supporting information to generate responses.”

Strengths and weaknesses: LLMs

“One of the biggest strengths of LLMs is their ability to meet the exact specificity of a user’s query,” says Ridgway. “They can take complex topics and provide clear, highly tailored answers in seconds.”

At the same time, LLMs still have limitations.

“They can lack real-time accuracy, struggle with local or hyper-niche data, and don’t always cite sources. Human verification remains important when precision matters,” Ridgway adds.

While LLMs can only generate answers based on what they were trained on, retrieval-augmented generation (RAG) helps close that gap. (Image: Adobe)

“Additionally, the range of answers is much broader with LLMs because it depends on how the model is trained, what supporting information it has access to, and its contextual reasoning.”

While LLMs can only generate answers based on what they were trained on, retrieval-augmented generation (RAG) helps close that gap. RAG pairs an LLM with a live retrieval system, allowing it to pull in up-to-date, authoritative sources before generating an answer — resulting in more accurate, grounded responses.

Here are LLM’s key strengths and weaknesses:

Strengths:

• Allow for ongoing discussion for deeper understanding
• Can clearly synthesize information using clear, human-like responses
• Understand the meaning behind searches, natural language, and complex queries
• Can personalize answers based on context

Weaknesses:

• Can “hallucinate” and produce inaccurate information
• Are less transparent with sources
• May be trained on outdated datasets
• Can oversimplify answers to more complex subjects
• Limited by the sources used to train the model

Strengths and weaknesses: Traditional search results

Traditional search results are reliable tools for retrieving high-quality information quickly, giving users control to scan multiple sources.

However, getting a quick answer (or understanding the true intent) can be difficult — and these systems lack the level of personalization LLMs provide.

“Traditional search engines like Google do personalize results, showing different sponsored or organic shopping results depending on location, browsing history, or prior interactions,” says Ridgway.

“But this personalization is generally less specific than what LLMs can achieve.”

Here are the traditional search engines’ key strengths and weaknesses:

Strengths:

• Clear transparency on sources and links
• Regularly updated index offers real-time, diverse sources of information
• Highly accurate for timely, news-driven, or fact-sensitive queries
• More trustworthy for YMYL (Your Money, Your Life) topics
• Users can explore sources of information for more reliable research, rather than a synthesized answer

Weaknesses:

• More time-consuming, requiring sifting through multiple sources
• Results can be buried by ads and SEO optimized content
• Less personalized
• Doesn’t understand the intent behind language as well as LLMs

The future of search: How LLMs and traditional search will work together

LLMs are changing the way users search. As user behavior shifts, most searches involve both systems, each serving different needs.

“The line is becoming increasingly blurred as Google integrates AI Overviews, AI mode, and other generative experiences directly into search results,” says Ridgway.

LLMs excel at:

• Brainstorming and idea generation
• Synthesizing information
• Clarifying complex concepts
• Assisting with tasks (drafting, coding, planning)

Traditional search excels at:

• Real-time and breaking information
• Product evaluation and reviews
• Local queries
• High-trust, verifiable research

This creates a split journey: users turn to AI-powered search for the early and in-between moments (idea exploration and decision support) and rely on search engines for the “main event” (deep research, product comparison, and news).

“LLMs are shortening and transforming the user journey,” Ridgway explains.

“People still double-check answers through traditional search because they don’t fully trust AI-generated responses, or because some areas — like local SEO — are less developed.”

What this means for industries

Entire industries will see user journeys reinvented.

“In ecommerce, websites currently act as catalogs users browse,” says Ridgway. “Soon, LLMs will pull data across catalogs and recommend the best-fit products directly within the chat platform.”

She adds that early signs already exist in ChatGPT’s Product Feeds and Instant Checkout.

How LLMs will impact the future of SEO

Looking ahead, Ridgway predicts search visibility will depend more on semantic relevance, query fan-out, structured data, and authority than keyword density.

“In many ways, SEO is evolving into a space where brands must create content that LLMs trust enough to quote,” says Ridgway.

“LLMs are redefining SEO. It’s no longer just about ranking a page, it’s about being part of the answer itself.”

Here are some key examples of how LLMs will impact SEO:

1. Shift from keyword ranking to topic authority

While topic authority is important for organic search, LLMs prioritize expertise and clarity over exact-match keywords.

2. Rise of AI-ready content

Content that is well-structured, factual, up-to-date, original, and concise is more likely to be used by AI platforms.

3. Traditional rankings will still be a priority

AI-driven search results still rely on the top-performing content, so strong SEO translates to higher LLM visibility.

4. New performance metrics

Expect emerging KPIs like AI citation share, zero-click visibility, and answer-presence rates to become part of standard SEO reporting.

How to optimize content for LLMs and traditional search results

“To perform well in both traditional search and LLM-driven experiences, focus on clarity, structure, and direct, conversational answers,” says Ridgway.

“Clean headings, evidence-based insights, and schema markup help both humans and models understand your content, while concise summaries increase your chance of being cited.”

Here’s a complete list of ways to optimize content for LLMs and traditional search results:

1. Use direct answers

Lead with clear definitions or short explanations. LLMs and search engines both look for concise answers to search queries that they can surface quickly.

2. Focus on formatting and structure

Use H1s, H2s, H3s, bullet points, tables, and short paragraphs to organize text. Strong structure makes your content easier for models to parse and improves user experience.

3. Leverage conversational language

LLMs can effectively interpret intent. Mirroring how users naturally phrase their questions helps your content match the long-tail, semantic queries these models surface.

“The way queries are framed now differs significantly between traditional search and LLMs, which changes the type of response users receive,” says Ridgway.

4. Account for hyper-specific questions

LLMs excel at very specific queries, whereas search results are broader.

“This is something SEOs will increasingly need to match, moving beyond generic rankings to content that addresses niche, detailed queries, like ‘stylish, neutral sneakers for standing for 12 hours under $125 including tax and shipping,’” Ridgway explains.

“That level of specificity is where LLMs shine and where traditional SEO may lag.”

5. Center content around information gain

“Information gain” refers to content that provides unique and valuable insights, data, and quotes that can’t be found anywhere else on the search engine results page (SERP). This is the type of value that AI tools can’t provide on their own, giving you the opportunity to be a cited source.

Proprietary information is becoming the real differentiator.

“As LLMs increasingly handle generic ‘what is’ or ‘how to’ queries on their own through ungrounded responses, brands must create unique insights, first-party data, and original perspectives,” says Ridgway.

“These are the types of content that models cannot infer and will continue to reference in answers.”

6. Use schema markup

Schema markup, or structured data, is code you can use on your site to help search engines better interpret your content.

This increases your odds of appearing in featured snippets, AI overviews, and other rich results.

Further reading: Schema Markup: What it is, How to Audit + Why it Matters

7. Expand your focus on brand awareness

As Ridgway notes, “For SEOs representing a brand, the scope of optimization now goes beyond the website.”

That’s why it’s important to educate your company on brand-to-entity connections, because LLM visibility will increasingly be shaped by overall brand awareness.

“Pages you may have overlooked, like the About Us page, can now carry significant weight, and campaigns outside your direct control, like PR placements, aggregator references, and mentions across trusted sites, become essential,” she adds.

The goal is to ensure your brand is cited and trusted across the spaces where your audience interacts and searches.

SEO is no longer just about optimizing for Google; it’s about making your brand recognizable, authoritative, and referenceable across the broader digital ecosystem.

8. Regularly refresh content

Frequent updates ensure your content remains accurate, timely, and competitive — and increase the likelihood it will be trusted and cited by AI systems.

9. Consider AI chatbot functionalities

Using chatbots that are trained with your brand’s information can enhance user experience and provide further data to learn what customers are looking for.

The takeaway

Traditional search results and LLMs shouldn’t be rivals. Instead, search behavior (and the search landscape itself) is changing.

The two systems will increasingly work together to provide the most efficient and thorough search experience possible.

This does mean SEO strategies will need to adapt. Now, content must be optimized for SEO as well as LLMs to stay visible across both ecosystems.

“The line is becoming increasingly blurred as Google integrates AI Overviews, AI mode, and other generative experiences directly into Google search results.”

“Over time, users aren’t just typing keywords, they’re asking conversational, nuanced questions that can be answered in a single response rather than clicking through multiple web pages.”

Need help getting your content ranked across both LLMs and traditional search?

HawkSEM is a top digital marketing agency at the forefront of the AI shift — and our clients see an average 4.5X ROI across SEO, PPC, and social media.

Reach out today to see how we can elevate your visibility.