Will Chatbots Eat SEO?

Large Language Models (LLMs) are based on transformer technology and their operating principle can be described as follows:

• Models are trained on large data corpuses - collections of documents, which allows them to identify the likelihood of one word appearing after another.
• The models use the context of surrounding words rather than the sequence of words as implemented in conventional text predictions such as T9.
• The generative model creates text by extrapolating it to a given length, attempting to predict the next token in the sequence.

This is similar to how autocomplete, suggestions, and other similar algorithms work. The more often a sequence occurs, the higher the likelihood of specific words appearing in further generation. However, it is important to note that generative language models do not write text the way humans do: they only emulate probabilistic dependencies based on training data. Over time, they lose confidence, especially if they go out of context, which can lead to ridiculous results. This is noticeable, for example, in search engine suggestions, where adding each new word to a string can lead to inadequate predictions.

The main problems of large language models

One of the main challenges large language models face is the quality of the training data. Models are trained on ready-made collections of documents, such as Wikipedia, blogs, various Internet archives and mass media. Can these data be considered ideal? Of course not. These corpora reflect only a small portion of the information available online and quickly become outdated.

Additionally, the data used for training is often biased. This is a reflection of the interests of the active part of the Internet audience, and not of the whole society. Consequently, the information generated by such models does not always reflect the full picture.

Another problem is that the model does not produce "coherent text" in the usual sense of the word. In fact, this is just a random combination of fragments that sound logical at the level of probability, but in reality do not always make sense. Models do not understand the meaning of the texts they generate and only reproduce fragments of other people's statements.

It is also worth noting that training large language models requires significant financial and environmental costs. This is especially true in the context of the current environmental agenda, which can become a serious limitation for the further development of such technologies.

Finally, another issue is the ripple effect of generated content. What one model generates becomes part of the training material for another, and so on. This leads to duplication of information and its “knocking out” of the real context, creating a kind of closed chain.

How can you use LLM in SEO?

You may have come across articles that claim that generating content using LLM brings in a lot of traffic. But, in practice, it is not recommended to use this for serious business purposes:

• For serious projects: Content generated by chatbots is often meaningless, which can lead to lower conversions. Don't rely on this type of content as your primary strategy.
• Empty texts: Generating content that does not provide value will eventually be identified by search engines as spam, which will most likely lead to sanctions from search engines.

However, this does not mean that new technologies should be abandoned completely. You need to approach the use of LLMs wisely, understanding their capabilities and limitations. Let's look at where such models can be useful.

• Generation of short texts: The shorter the text, the more meaningful it looks. You can use chatbots to write meta descriptions, summaries, or short texts on catalog pages. However, such texts still require editing.
• Text content analysis: To audit the semantics of a topic, you can use models to analyze tens or hundreds of pages from search results to identify keywords. This approach allows you to quickly collect data, which significantly saves time.
• Summarizing texts: Generative models are good at reducing and squeezing information from long texts. If you need to create a condensed version of a large amount of material, this can be a useful tool.

Additionally, neural networks can be useful for image generation. In modern search engines, neural network algorithms work on the same principles. If you need to create a unique image that matches certain patterns, use this tool. However, remember that the generated images also require some work.

In conclusion, unless you have a clear content or idea, machine algorithms cannot replace real-life communication with users. It is important to understand that successful SEO always requires people who can transform information into high-quality and valuable content.

If you have any questions, do not hesitate to contact the SEO studio "SEO COMPUTER" by email info@seo.computer.

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