Google BERT Update: What It Is, Why Google Made It, and How to Use It in SEO

Google BERT is a language understanding system. Google announced it in October 2019. BERT stands for Bidirectional Encoder Representations from Transformers. That name sounds complicated, but the core idea is simple: BERT helps Google understand the full meaning of a sentence by looking at every word in relation to all the other words around it, not just reading left to right in isolation.

Before BERT, Google's systems mostly processed a search query word by word, often missing how small connecting words changed the overall meaning. This caused problems like:

BERT was Google's way of fixing this. It was described by Google as one of the biggest improvements to search in several years, and at launch it affected around 1 in 10 searches in English, later expanding to almost all English searches and many other languages.

To understand why BERT was needed, think about how much meaning can hide in small, simple words.

Take the search: "2019 brazil traveler to usa need a visa." The word "to" here matters a lot. It means a Brazilian traveling to the USA, not the other way around. Older systems often struggled with this kind of nuance, because they were not fully considering how each word connected to the words around it. This could lead to search results about US citizens traveling to Brazil, which completely misses the point of the search.

As people increasingly searched using natural, conversational language rather than short keyword phrases, this problem became more and more common. Google needed a system that could read a sentence more like a human does, understanding context and relationships between all the words at once, not just processing them one at a time in a fixed order.

So Google built BERT with one clear goal: understand the full context and meaning of a search query, including small connecting words, so it can match the searcher's real intent more accurately.

BERT works differently from older language processing methods, because it reads text in both directions at once. Based on what Google has shared, here is roughly how it works:

Reads text bidirectionally - Instead of reading a sentence only from left to right, BERT looks at the words before and after each word at the same time, to understand its full context.

Understands the role of small words - Words like "not," "to," "for," and "without" are properly weighed, since these words can completely flip or shift the meaning of a sentence.

Trained on huge amounts of text - BERT was trained using massive amounts of text from the web, which helped it learn general patterns of how language works, before being applied specifically to understanding search queries.

Applies to both search queries and passages - BERT does not just help Google understand what you searched for. It also helps Google better understand the content of web pages, so it can match search intent to the right paragraph or section of a page, not just the page as a whole.

Works alongside other ranking systems - BERT does not replace Panda, Penguin, Hummingbird, or RankBrain. It works alongside them, specifically improving how well Google understands the language in both the search query and the content being ranked.

People often mix up BERT with other well-known Google updates. Here is a simple comparison.

You cannot "optimize for" BERT directly, since it is a language understanding system, not a checklist. Instead, you write clearly and naturally, so both readers and Google's systems can easily understand your meaning. Here is how to do that in simple steps.

1. Write in clear, natural sentences

Avoid awkward, keyword-stuffed sentences that sound unnatural. Write the way you would actually explain something to a person, with proper grammar and clear connecting words.

2. Pay attention to prepositions and small words

Be precise with words like "for," "to," "without," and "not." These small words carry real meaning, and unclear phrasing can confuse both readers and search systems about what you actually mean.

3. Answer questions the way people actually ask them

Since BERT improved Google's ability to understand natural, conversational questions, structure some content directly around full questions, written the way a real person would ask them.

4. Focus on clarity over keyword density

You do not need to repeat your target keyword many times. Focus on explaining the topic clearly and completely, since BERT helps Google understand relevant content even when the exact keyword phrasing differs.

5. Structure content so specific sections answer specific questions

Because BERT also helps Google match search intent to specific passages within a page, organize longer content with clear headings for each sub-topic or question, so Google can identify and surface the most relevant section.

6. Proofread for grammar and clarity

Since BERT is specifically about language understanding, poorly written, grammatically confusing content is more likely to be misunderstood. Clear, well-edited writing genuinely helps both readers and Google understand your content correctly. For a resource like SubmitWell, this means writing directory and blog content with careful, precise language, so guides on topics like submission requirements or pricing are not misread or misunderstood by search systems trying to match a very specific user question.

Imagine someone searches: "can a startup submit to a directory without a working website."

The word "without" is the key here. The person is specifically asking whether a working website is required, and they likely do not have one yet, or are unsure if they need one first.

Before BERT, Google's systems might have focused mainly on the words "startup," "submit," "directory," and "website," and could have returned general pages about submitting startups to directories, even ones that assume you already have a live website, missing the actual question being asked.

With BERT, Google can better understand that "without" changes the entire meaning of the search. It can then prioritize a page that specifically addresses whether a website is required for submission, rather than a generic page that simply mentions all the same keywords.

This shows the real value BERT brings: small words can completely change what someone is really asking, and BERT helps Google catch that.

Pros

Cons

The BERT update pushed Google's understanding of language much closer to how a human actually reads and interprets a sentence. Small words that used to be overlooked, like "for," "to," and "without," now genuinely matter to how a search is understood. More than five years later, this shift toward deep language understanding continues to shape how Google, and modern AI systems generally, process written content.

If you are creating content in 2026, the lesson is simple. Write clearly, naturally, and precisely, the way you would actually explain something to a real person. Whether you run a blog, a SaaS product, or a startup directory, well-written, grammatically clear content is still the safest way to be correctly understood, both by your readers and by the systems ranking your pages.