Are LLMs And Search Engines The Same?

AI large language models (LLMs) like ChatGPT have taken the world by storm and are being integrated into workflows, platforms, and software to make us more efficient and productive.

ChatGPT is like the new Frank’s RedHot sauce; people use it for everything.

Search engines like Google and Bing have changed to incorporate AI chatbots into their algorithms, but do search engines and AI LLMs now serve the same purpose?

Here’s a comparison between AI LLMs and search engines:

Function

AI large language models are designed to generate human-like text based on the input they receive. They can answer questions, draft content, provide suggestions, assist in tasks, and more.

While initially ChatGPT did not search the web, it can now browse the internet if you’re on version 4 or Plus. However, some AI LLMs generate responses based on a vast amount of training data they’ve been provided.

Search engines are designed to index and retrieve information from the web.

They provide links to web pages, documents, images, videos, etc., that are relevant to a user’s query – but this is changing with the incorporation of AI chatbots into the search engine results and the use of AI in search engine algorithms.

Here is a list of the most popular providers and LLM models:

Data Sources

LLMs are trained on a large set of data.

They rely on the data on which they were last trained. Hence, they might not have real-time or the most up-to-date information sometimes if they are not accessing the web and are not constantly trained on new datasets.

Search engines continuously crawl and index the web, which helps them find the latest available information on a topic, assuming the content has been indexed.

Interactions

LLMs are designed for more conversational interactions and can engage in back-and-forth dialogue and generate text in a context-based manner.

Search engines primarily provide a one-way interaction.

You enter in a query, and the search engine provides relevant links to relevant content that might satisfy your information needs, and now with Google and Bing conversational results.

Output

LLMs provide generated text as an answer and even imagery. The quality and accuracy of the answer can vary and be questionable, but it’s usually in a coherent and human-readable format.

Search engines provide links or references to external sources.

Users then have to click and read these sources to find specific answers, but this has changed thanks to Google’s SGE (Search Generative Experience) and the incorporation of a next-generation large language model (LLM) from OpenAI into the Bing search results.

GPT-4 is trained on a large dataset of text and code, and it is able to understand and generate human language in a more human-friendly and informative way.

Recent developments with the latest version of Open AI’s ChatGPT 4 can access and process information through Bing, which allows it to provide up-to-date and relevant search results.

In addition to GPT-4, Bing also uses other AI technologies to power its search results for the purposes of ranking websites, identifying and filtering out spam, and generating personalized search results.

AI plays a vital role in powering Bing’s search results. By using GPT-4 and other AI technologies, Bing is able to provide users with a more comprehensive, informative, and personalized search experience.

Reliability And Accuracy

LLMs are prone to errors because when they generate answers, there’s a risk of inaccuracies or outdated information – especially if they haven’t been trained on newer data. Some of the drawbacks of LLM’s include:

• LLMs can be fooled by misinformation and propaganda.
• They can be computationally expensive to train and deploy.
• They may not be able to understand the context of web pages as well as humans can

Search engines provide direct links to sources, which allows users to verify the accuracy of information. However, the order and visibility of results might be influenced by various algorithms, SEO practices, and potential biases.

Will LLMs Take Away Search Engine Market Share?

LLM models are not yet having a significant impact on search engine market share.

Google is still the 800-pound gorilla that continues to dominate the global search engine market share, with over 90%. Bing comes in a distant second with only a small percentage of the search engine market share.

However, AI LLMs will continue to revolutionize the search engine industry in the years to come.

LLMs can provide more comprehensive and informative answers to search queries. They can also generate new and creative content and imagery, and do many other things like code and summarize paragraphs, to name just a couple.

With the advancements of LLMs, this could potentially lead to a shift in market share toward search engines that are powered by AI LLMs. In my opinion, they won’t take over search engine market share at a dominant level – though they may disrupt it.

It’s unlikely that LLMs will entirely replace search engines, at least in the near future, for several reasons:

• Up-to-date information: Search engines offer access to the latest information available on the web. LLMs might not have real-time data unless they’re consistently updated or access the internet.
• Depth and breadth: While LLMs can provide quick answers for in-depth research or diverse viewpoints on a topic, search engines provide a range of sources, but some LLMs like PALM2 and Google Bard do provide sources from where they get their information, unlike OpenAI’s ChatGPT.
• Different use cases: LLMs are great for conversational AI, tutoring, content generation, coding, and more, while search engines are essential for research, news, and broad information discovery. With the incorporation of LLMs into the search results, this will continue to change over time.

Wrapping Up

While AI LLMs have the potential to revolutionize search, they will not replace search engines completely. AI LLMs may continue to be used to enhance search engines in the future to:

• Generate more informative and comprehensive search results.
• Personalize search results based on a user’s individual needs and interests.
• Identify and filter out spam and misinformation.
• Provide users with a more natural and conversational search experience.

The future of search will likely be a hybrid of traditional search engines and AI LLMs.

AI LLMs will continue to play an increasingly important role in search, but they are not likely to completely replace search engines.

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