LLM in future AI-driven search engines

How will generative AI affect the way we access information and its importance for future search engines? In this article, you'll learn more about the differences between traditional Googling and asking questions through large language models (LLM) like ChatGPT. You'll also get tips on how to prepare for future search engines as generative AI becomes increasingly important in our daily lives and gets integrated into search engines.

What does LLM mean?

The rise of large language models (LLM) like ChatGPT has changed how users find information. LLM stands for "Large Language Model." It is a type of artificial intelligence trained on large amounts of text data to understand and generate human language. Examples of LLMs include GPT-3 and GPT-4, which can perform a range of language tasks such as text generation, translation, summarization, and conversation. More and more people are turning to AI assistants for direct, personalized answers, rather than typical Googling through conventional search engines. This shift creates an exciting new channel for information that marketers need to consider to future-proof their presence in new search services. Brands must ensure that their content and profiles are presented accurately and are relevant to the specific questions (prompts) that users ask.

Differences between traditional Googling and LLM technology

When it comes to searching the internet, traditional keyword search methods are far from the only option. While these search engines deliver results based on simple keywords, LLM systems, which stand for large language models, can understand the context and deeper meaning of the user’s queries. Through advanced language understanding and generative AI technology, LLM systems can directly deliver more relevant and personalized answers instead of just listing links to websites.

The positive effects of LLM integration in search engines

Deeper understanding of the user’s true intent behind the search query.
Ability to handle complex and natural questions.
More context-based and customized search results.
Continuous learning and improvement of algorithms.

Integration of LLM in search engines improves the User Experience

There are already functioning examples of how language models and search engines can complement each other effectively. Services like Google’s AI Overview (launched in the USA) and Microsoft’s Copilot integrate LLM-based features that provide users with more natural and context-based answers while leveraging search engines’ ability to index and search the web in real-time. Perplexity.ai is another example of how LLM and search engines can work together to give users an optimized experience. Perplexity.ai uses its deep text understanding not only to generate relevant answers but also to evaluate and verify sources. By analyzing factual information, language usage, the sender, and other quality indicators, the system can assess the credibility of sources.

ChatGPT is not a search engine

It’s important to understand that LLMs like ChatGPT are AI systems trained on massive amounts of data. Their purpose is to generate natural language in response to various language-based tasks, such as answering questions and writing texts. They work by analyzing and understanding the context of a given conversation or task and then producing a relevant response based on how words are commonly used together.

The process itself is markedly different. The language model uses its internal understanding to generate a response, rather than searching through external sources. Unlike search engines like Google, language models have no direct connection to an indexed database of web pages. They base their responses on the information they’ve been trained on, not on searching through and compiling information from different websites and listing these in a search result.

This is an important difference because it means that language models have limitations compared to search engines. The data they have been trained on can be outdated or contain inaccuracies, and they can sometimes produce incorrect or inappropriate responses.

AI search engines of the future

Websites that have previously relied heavily on traffic from informative content will face new challenges as LLM technology becomes increasingly dominant. LLM gives users direct access to the searched information without requiring them to visit the website. This can partially or completely reduce traffic to these websites. Although this can improve the user experience through quick access to relevant information in the right place, it also leads to reduced traffic to the websites.

From a commercial perspective, it is interesting to see how e-commerce and conversions will be affected as LLM technology is used not only for informative searches. LLM technology has the potential to revolutionize product comparisons and online shopping experiences. Websites that have previously relied on traffic from information-seeking users must undergo strategic changes to meet these developments. It will also impact how content is created and how search engine optimization (SEO) is approached.

From a commercial perspective, it is interesting to see how e-commerce and conversions will be affected when the LLM technology is not only used for informational searches. LLM technology has the potential to revolutionize product comparisons and online shopping experiences. Websites that previously relied on information-seeking traffic must undergo strategic changes to meet these developments. It will also affect how content is created and search engine optimization (SEO) is handled.

In e-commerce, LLM can play a central role in comparing products and in carrying out the entire shopping process. By quickly comparing specifications, reviews, and prices from different sources directly in the search results, LLM can make the shopping process smoother and more efficient for users. Additionally, language models can be integrated directly into e-commerce systems, allowing users to complete the entire purchase process—from product selection to payment—without leaving the search results. This would create an even more seamless and user-friendly shopping experience.

Structured data such as product information, customer reviews, prices, and specifications will be essential for LLM to provide users with relevant and useful answers directly in the search results. Without access to well-structured data, LLM-based services will struggle to provide accurate product information and detailed comparisons that users expect. Websites that previously relied on unstructured informational content now need to invest more resources in structuring and tagging their data to ensure that LLM-based systems can use and present this information in a user-friendly way.

When it comes to paid advertising (SEM), LLM technology can also have a significant impact. By using LLM, advertisers can create even more personalized and context-adapted ads that better match users’ intentions and needs. This can significantly improve ad effectiveness.

AI and LLM-based searches will greatly influence how we search for information in the future. For those willing to adapt, there are great opportunities. It is truly exciting to see how the future takes shape!

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