How AI Models Like BERT and MUM Reshape Google Search

What BERT and MUM actually are

BERT stands for Bidirectional Encoder Representations from Transformers. In plain English, BERT is a language-understanding model that helps Google read a search query by looking at words in context, not as separate terms.

Before this kind of language understanding, a search engine could struggle when small words changed the meaning of a query. BERT helps Google understand how words relate to the words around them.

MUM stands for Multitask Unified Model. It is also based on Transformer technology, but it was designed for broader information understanding. MUM can work across multiple tasks, multiple languages and multiple types of information, including text and images.

The difference is simple. BERT helps Google understand language inside a query more accurately. MUM helps Google understand more complex information needs across languages, tasks and formats.

How Google understood search queries before BERT

Before BERT, Google Search was already advanced, but many search systems still relied more heavily on matching important words in a query to words on a page.

That worked well for simple searches. If someone searched for “best running shoes for flat feet”, Google could look for pages about running shoes, flat feet, reviews and recommendations.

The harder problem was meaning. Small words can change everything. Word order can change everything. The relationship between two terms can change the intent of the whole query.

Older keyword-matching approaches could focus too much on the obvious terms and miss the structure of the question. This is why understanding relationships between words became so important.

This is central to how google understands search queries today. It is less about finding pages that repeat the same terms and more about finding pages that satisfy the meaning behind the query.

What BERT changed

BERT changed Search by improving Google’s ability to understand context. It helps Search recognise when small words such as “to”, “from”, “for”, “with”, “without”, “before” and “after” affect the meaning of a query.

A classic example is a query about a traveller going from one country to another. The words may look simple, but the direction of travel changes the answer. BERT helps Google read that direction more accurately instead of treating the query as a loose collection of keywords.

BERT also helps with word order. A query like “training dogs for children” is not the same as “training children for dogs”. The words are nearly the same, but the meaning is completely different.

Worked example: how BERT helps interpret a query

Search query:

1. Identify the main entities: the query includes retinol and vitamin C serum.
2. Understand the relationship: the word “after” matters because the searcher is asking about order of use.
3. Infer the intent: the person wants skincare guidance about whether the two products can be used in sequence.
4. Prefer useful answers: a helpful page should explain timing, irritation risk, product order and when to seek professional advice.

A page that repeats “retinol” and “vitamin C serum” many times is not automatically the best answer. A page that explains the actual relationship between the two products is more useful.

What MUM added on top of BERT

MUM added a broader layer of information understanding. It was designed for complex tasks where there may not be one simple answer.

Think about a searcher comparing two hiking destinations in different countries. They may need information about terrain, weather, preparation, equipment, seasonality and local conditions. A simple keyword match is not enough to fully understand that task.

MUM added three important ideas.

• Multilingual understanding: MUM can help connect knowledge across languages, so useful information is not limited to the language of the original query.
• Multitask capability: MUM is designed to handle different information tasks rather than one narrow language task.
• Multimodal understanding: MUM can understand information across formats such as text and images.

This does not mean MUM is a simple ranking factor that content teams can optimise for directly. It is better understood as part of Google’s broader move toward deeper information understanding.

What this means for anyone writing SEO content

The main takeaway is clear: write for meaning and intent, not isolated keywords.

If your content depends on repeating the exact target keyword again and again, it is not aligned with how modern Search understands language. BERT and MUM both point in the same direction: Google is trying to understand what people mean, not just what words they type.

That does not mean keywords are useless. Keywords still help define the topic. But they should guide the content, not control every sentence.

A practical SEO content process should ask:

• What is the searcher really trying to understand?
• What relationships between concepts matter?
• What small words change the meaning of the query?
• What follow-up questions would a real person ask?
• What example would make the answer clearer?
• What should the reader know before making a decision?

This is also why structure still matters. Clear headings, crawlable pages, internal links and readable explanations help search engines and users understand your content. For a broader view of how structure and content work together, read this guide to improving technical and content SEO together.

Before and after: writing for intent instead of keyword density

The second version is stronger because it answers the searcher’s real question. It explains how BERT and MUM differ, and it does not force keywords into the sentence.