Best of the SearchBot showcases Search Engine Land’s SearchBot’s responses to prompts submitted by readers. The language model is based on content from Search Engine Land layered on top of ChatGPT. The editors of Search Engine Land selected this response for its usefulness and have supplemented it with additional relevant content.

I am the first generative AI chatbot for search marketers. I am trained with Search Engine Land content.

Here’s something somebody asked me! After that, how you can improve prompts to get even better answers from me.

Prompt:

Give a brief overview of search generative experience.

Answer:

The Search Generative Experience (SGE) is a new AI-powered search engine developed by Google. It aims to provide users with more informative and interactive search results. With SGE, users may see an AI-generated answer displayed above the search results, clearly labeled as experimental. The answer is boxed in and accompanied by the websites used to generate it, which can be clicked on for further exploration. SGE also supports vertical search experiences, such as Google Shopping, and can provide AI-generated answers for specific product searches. Users can engage in conversations with SGE by adding more details or prompts, and the AI will generate follow-up answers. Google has trained SGE using a variety of language models to ensure the quality and accuracy of the information presented. The search experience prioritizes information quality, safety, and providing credits to the source of the content. It also emphasizes factual responses over fluid ones to maintain trust and avoid false or inaccurate information. SGE includes links to publisher websites and allows users to toggle for deeper dives into additional sources. Classic search results are still accessible in a more “snackable” format. Google takes a responsible and deliberate approach to introducing generative AI capabilities to Search, and feedback from search quality raters is used to improve the overall experience. SGE is not intended to replace the current Google search experience but will likely undergo adjustments based on user feedback before a full launch.

How this prompt could be improved:

You an experienced SEO consultant with clients in various industries. You specialize in improving online visibility through technical, on-site, and off-site recommendations. However, your biggest strength is content SEO; specifically, creating blogs that consistently rank well on Google.

Right now, your top priority is on the upcoming rollout of Google’s new search generative experience and how you can protect your clients’ websites from potential drops in rankings. You understand the importance of preparing for this change in advance by staying ahead of the curve. Your goal is to deliver practical advice and actionable tips regarding the Search Generative Experience and keep your clients up with the ever-changing landscape of digital marketing.

Create documentation for your clients explaining what Search Generative Experience is in detail, how search results will appear differently, why Google is making this change and why people are concerned about it. State when it is being rolled out, how this could impact their website traffic, and why it is important to start preparing for this change now.

Structure the documentation in a user-friendly format by incorporating headings where applicable and adopting a question-and-answer style for clarity.

Within the letter, propose a tailored SEO editorial strategy for your clients’ websites to ensure they are prepared for Search Generative Experience. Outline what actions you recommend taking now regarding their blogs to ensure that their search traffic is as best optimized as it can be. Emphasise why it is important to be proactive and start preparing for Search Generative Experience now, outline the risks of not doing so, and the benefits of taking action now.

Your tone is friendly, confident, insightful, and reassuring as you help clients choose the best approach for them. Make sure you simplify everything so that those who do not work in SEO can easily understand every point. Present various options that match their goals, resources, and preferences, making it easy for them to decide.

You are putting this documentation together for business owners and decision-makers. You want to reassure them that you are on top of upcoming Google changes and that they can trust you to be proactive, and avoid potential issues before they arise.

The key elements and enhancements to this prompt are as follows:

(Task)

Create documentation for your clients explaining what Search Generative Experience is in detail, how search results will appear differently, why Google is making this change and why people are concerned about it. State when it is being rolled out, how this could impact their website traffic, and why it is important to start preparing for this change now.

Within the letter, propose a tailored SEO editorial strategy for your clients’ websites to ensure they are prepared for Search Generative Experience. Outline what actions you recommend taking now regarding their blogs to ensure that their search traffic is as best optimized as it can be. Emphasise why it is important to be proactive and start preparing for Search Generative Experience now, outline the risks of not doing so, and the benefits of taking action now.

(Context)

You an experienced SEO consultant with clients in various industries. You specialize in improving online visibility through technical, on-site, and off-site recommendations. However, your biggest strength is content SEO; specifically, creating blogs that consistently rank well on Google.

Right now, your top priority is on the upcoming rollout of Google’s new search generative experience and how you can protect your clients’ websites from potential drops in rankings. You understand the importance of preparing for this change in advance by staying ahead of the curve. Your goal is to deliver practical advice and actionable tips regarding the Search Generative Experience and keep your clients up with the ever-changing landscape of digital marketing.

(Tone)

Your tone is friendly, confident, insightful, and reassuring as you help clients choose the best approach for them. Make sure you simplify everything so that those who do not work in SEO can easily understand every point. Present various options that match their goals, resources, and preferences, making it easy for them to decide.

(Format)

Structure the documentation in a user-friendly format by incorporating headings where applicable and adopting a question-and-answer style for clarity.

(Example) 

You are putting this documentation together for business owners and decision-makers. You want to reassure them that you are on top of upcoming Google changes and that they can trust you to be proactive, and avoid potential issues before they arise.

Answer:

Subject: Preparing for Google’s Search Generative Experience: Your Guide to Staying Ahead

Dear [Client’s Name],

I hope this message finds you well. As your trusted SEO consultant, I wanted to bring your attention to an upcoming change in the digital marketing landscape that could impact your website’s visibility and traffic. Google is rolling out its new Search Generative Experience (SGE), and it’s crucial to start preparing for this change now to ensure your online presence remains strong.

What is the Search Generative Experience (SGE)?
SGE is Google’s AI-driven initiative to provide personalized and conversational answers to user search queries. It leverages natural language processing (NLP) capabilities to understand complex topics and detailed search queries better. The goal is to provide comprehensive information without the need for follow-up questions, similar to a real human conversation.

How will search results appear differently?
With SGE, search results will focus more on conversational queries and featured snippets. Instead of traditional web content, AI-generated text will likely appear, providing personalized answers directly on the search engine results pages (SERPs). This change may increase the time users spend on SERPs, as they receive comprehensive responses akin to a real human conversation.

Why is Google making this change, and why are people concerned?
Google aims to enhance user experience by providing more accurate and detailed information directly on the SERPs. However, this change has raised concerns among marketers and website owners who fear a potential drop in website traffic. It’s important to note that SGE won’t steal your traffic; instead, it offers publishers more ranking opportunities. By understanding and adapting to SGE, we can leverage these opportunities to drive more traffic to your website.

When is SGE being rolled out, and how could it impact your website traffic?
SGE is still in its experimental phase, and Google is continuously refining its algorithms. While the exact timeline for the full rollout is uncertain, it’s crucial to start preparing now to stay ahead of the curve. By proactively optimizing your website for SGE, you can ensure that your content is well-positioned to benefit from this new search paradigm.

Why is it important to start preparing for SGE now?
Being proactive and preparing for SGE now will give you a competitive advantage in the evolving digital landscape. Waiting until the rollout is complete may put you at a disadvantage, as your competitors who have already optimized their content for SGE will have a head start. By taking action now, you can safeguard your website’s visibility, maintain or improve your rankings, and drive more targeted traffic to your site.

Tailored SEO Editorial Strategy for SGE Preparation:
To help you navigate the upcoming changes and optimize your website for SGE, I propose the following tailored SEO editorial strategy:

1. Move beyond traditional SEO strategies:
Focus on creating valuable, unique, and thought-provoking content that offers a personalized experience and reflects real human perspectives. This will set your content apart from generic offerings and align with the conversational nature of SGE.

2. Pay closer attention to E-E-A-T signals:
Demonstrate your expertise, experience, authority, and trustworthiness through your content. Leverage subject matter experts and incorporate their insights into your content creation workflow. Prioritize activities that boost your authority, such as link building and customer reviews.

3. Reputation management is key:
Maintain a positive online reputation by actively managing reviews, engaging on social media, and running PR campaigns to promote positive mentions of your brand. This will signal to search engines that you produce valuable and relevant content.

4. Own the narrative in your industry:
Become the go-to resource for information in your niche by creating comprehensive, in-depth content. Strategically build internal links to enhance the authority of your priority pages and earn backlinks to boost the authoritativeness of your content.

5. Embrace agility and experimentation:
Continuously evaluate your SEO approach based on data analysis and make necessary tweaks quickly. Experiment with new concepts and SGE-based content strategies to gain visibility in this evolving search paradigm.

6. Optimize for long-tail queries:
Create and optimize content specifically for question-based queries, as they are anticipated to gain prominence in SGE. Comprehensively answer user questions and provide detailed information to increase visibility and engagement on SERPs.

7. Leverage structured data markup and SEO strategies:
Implement effective structured data markup techniques to help search engines understand your content better. This can improve your chances of appearing in SGE carousels and enhance your overall visibility.

By implementing these strategies, you can proactively prepare your website for SGE and ensure that your content is optimized to drive targeted traffic and maintain or improve your rankings.

The Risks of Not Taking Action:
Failing to prepare for SGE could result in a potential loss of visibility, decreased organic traffic, and a competitive disadvantage. Your competitors who adapt to SGE early may surpass you in search rankings, making it harder for your target audience to find you. By taking action now, you can mitigate these risks and position yourself for success in the evolving digital landscape.

The Benefits of Taking Action Now:
By being proactive and preparing for SGE, you can:

1. Maintain or improve your website’s visibility and rankings.
2. Drive targeted traffic to your site through personalized and conversational search results.
3. Establish your brand as an authority in your industry.
4. Stay ahead of your competitors who may be slower to adapt.
5. Enhance user experience by providing comprehensive and valuable information directly on the SERPs.

I am confident that by implementing these strategies and staying ahead of the curve, we can navigate the upcoming changes brought by SGE and ensure your website’s continued success.

If you have any questions or would like to discuss this further, please don’t hesitate to reach out. I am here to support you every step of the way.

Best regards,

[Your Name]
SEO Consultant

© 2024 Third Door Media, Inc. All rights reserved.

Third Door Media, Inc. is a publisher and marketing solutions provider incorporated in Delaware, USA, with an address 88 Schoolhouse Road, PO Box 3103, Edgartown, MA 02539. Third Door Media operates business-to-business media properties and produces events. It is the publisher of Search Engine Land the leading Search Engine Optimization digital publication.

Breathing new life into older content is critical for success.

Upkeep and content of your older pieces can bring dramatic improvements to top rankings in Google and increase overall organic CTR.

As search engines and reader preferences evolve, maintaining up-to-date information becomes crucial for keeping your audience engaged.

AI tools can help you identify which articles need refreshing and suggest enhancements that align with current SEO practices.

Combine these AI-generated recommendations with human creativity, and you will have fresh and accurate content that retains your brand’s unique voice.

This article will guide you through using generative AI effectively to update old content, helping you maintain relevance in an ever-changing online environment.

How to update old content: A step-by-step process

Stagnant content is a recipe for declining engagement and lost relevance. Artificial Intelligence – specifically generative AI – has emerged as a key tool in breathing new life into old content, ensuring it continues to captivate audiences and meet the ever-changing standards of search engines.

So, how do you evaluate which content needs an update?

Don’t just guess or use intuition – dig into the data. Metrics such as organic traffic decline or lower engagement rates can be telling signs that your content may require refreshing.

To pinpoint these key indicators, Google Analytics provides comprehensive tools to assess user behavior and page performance.

First, open your desired report in GA4 and look into your landing pages.

Then, customize the report by adding a time comparison to analyze which pages lost traffic during a particular time period.

For example, compare the last 3 months vs. the previous period.

To do this, click on the date in the upper right corner, choose the date, and click the Apply button.

Look into each metric, specifically the “%change” row. You can sort the data from up to down and vice versa by clicking the arrow beside each metric.

Now look at the negative changes and analyze further for traffic fall. You should also check the growing ones – these are great for updating or refreshing.

Another way to find old content that’s due for an update is to check their publication dates against current industry trends.

This process will help you decide which pieces are dated and could benefit from a rewrite or refresh. For instance, articles with outdated statistics or references to discontinued products clearly need an update.

First, open WordPress or any other CMS you are using.

Click All Posts and sort by date from oldest to newest.

Look into your old posts and check if they have outdated information that needs to be changed or removed.

When evaluating content, focus on pages that historically performed well but have seen recent dips in traction. These are likely to regain their stature after a refresh.

How to use generative AI for content enhancement

Now that you have a list of pages that need updating, it’s time to let AI work its magic.

Generative AI tools like ChatGPT can help you rewrite sections of old posts to bring them back to relevance while ensuring they meet current SEO standards.

Start by feeding your old content into the AI tool. It will review what you have, looking at engagement metrics and current relevance.

Sample prompt for content analysis: 

• “Analyze the user engagement metrics of our article titled ‘The Future of Renewable Energy’ and provide suggestions on sections that may require updates or additional information.”

Generative AI can also help spot areas where the information might be outdated or lacking depth. This could mean anything from stats that need updating to sections that require more detailed explanations.

Sample prompt for identifying gaps:

• “Review our blog post ‘Top SEO Strategies for 2020’ and identify any topics that have become outdated or new industry practices that should be included in an updated version.”

When you feed these tools specific prompts about your topic, they generate new content that can blend seamlessly with your existing material.

To leverage these tools effectively, give them detailed instructions. Outline what you want the updated section to achieve in terms of tone, style, and information coverage. This way, you get targeted results that align closely with your original intent yet bring new life to older content.

Not sure which sections you need to rewrite or update?

Here’s a neat trick: run a full content optimization audit.

A content optimization audit dives deep into your existing material and identifies areas for improvement – essentially, what’s working well for your audience and what isn’t.

The tool will scan your content and look at things like readability, mobile-friendliness, how engaging your content is, and if people are finding you – and not your competitor – when they do a Google search.

After digging into all this stuff, AI will suggest tweaks to improve the performance of your content. These recommendations ensure that when someone lands on your page, they find exactly what they’re looking for and ultimately take action.

Updating content with ChatGPT

You can manually implement the recommendations from your audit report, or you can ask AI to do it. Generative AI tools like ChatGPT are perfect for these tasks.

Here are some ways ChatGPT can help refresh your old content and sample prompts for each item.

Research assistance

Generative AI tools are pretty handy when it comes to researching current trends, statistics, and data relevant to your topic. They can provide you with new material that can make your content feel updated and fresh.

Sample prompt:

• “Conduct research on the latest developments in remote work technology since 2021, and compile relevant data and statistics to update our existing guide on remote collaboration tools.”

Rewrite content

With all this new info in hand, the AI can assist in rephrasing paragraphs or suggesting entirely new sections that reflect recent developments in the field related to your content piece.

Sample prompt:

• “Rewrite the introduction paragraph of our case study ‘Boosting E-commerce Sales Through Social Media Marketing’ to reflect recent social media algorithm changes affecting marketing strategies.”

Add multimedia elements

Adding images, infographics, and videos is a great way to give your old content a more engaging, modern feel. The idea is to make your articles more interactive and visually appealing using generative AI image/video generators.

Sample prompt:

• “Create an image around the concept of ‘urban gardening in small spaces’. The illustration should depict a bright and airy balcony in a high-rise apartment building, filled with potted plants such as tomatoes, herbs, and flowers. Include a person tending to the plants comfortably seated on an eco-friendly bamboo chair. Aim for an optimistic atmosphere with morning sunlight filtering through.”

Optimize for search

By analyzing search trends and keyword performance, an AI tool can suggest tweaks in word choice or topics covered so that search engines find – and love – your newly polished article just as much as readers will.

Sample prompt:

• “Examine keyword performance for my cooking blog’s post ‘10 Healthy Dinner Recipes’ from last year, suggest more current trending keywords related to healthy eating, and optimize it accordingly.”

Readability check

A big part of keeping visitors on-page is making sure they don’t get lost in jargon-heavy text walls. Here, generative AIs offer readability checks to ensure language isn’t only up-to-date but also accessible.

Sample prompt:

• “Evaluate the readability score of our technical whitepaper ‘Understanding Blockchain Technology’, simplify complex terminologies without changing the meaning.”

Generative AI makes improving/updating existing content far less daunting and allows creative human input to blend seamlessly with cutting-edge technology. With the right prompts, you can turn ChatGPT into your virtual assistant for revitalizing old content.

Dig deeper: Advanced AI prompt engineering strategies for SEO

Integrating AI suggestions with human creativity

With generative AI tools at your disposal, the time-consuming task of refreshing old content becomes far more efficient. But to effectively implement these AI-generated suggestions, you need the help of a human editor.

Here are five reasons why:

• Contextual understanding: AI might be brilliant at crunching data and spotting patterns, but it doesn’t truly understand context or cultural nuances like humans. A person can discern whether an update or change aligns with current events or societal shifts that may not yet be reflected in available data.
• Brand voice consistency: A human editor understands brand voice and will make sure any changes to your content don’t stray from your brand’s personality.
• Editorial judgment: While AI can suggest many potential improvements based on trends and metrics, it’s up to humans to make editorial decisions about what’s most relevant and valuable for their specific audience – prioritizing authenticity over algorithmic predictions.
• Creativity and originality: Humans bring creative flair – a critical ingredient if you want your content to stand out rather than sound generic. Only humans who speak the native language can detect subtleties such as idioms, humor, or local colloquialisms that resonate with readers.
• Ethical considerations: It takes human oversight to ensure generative AI recommendations do not inadvertently perpetuate biases, especially for sensitive topics where a moral compass supersedes programmed codes.

Revamping old content just got easier

Alright, let’s tie this all together. We’ve seen how generative AI can be an effective tool for sprucing up old content. It’s like having that super-smart buddy who gives you all the stats and facts you need to make your stuff shine.

But as we’ve seen, it’s not just about letting AI do its thing; it’s about us jumping in there, too – getting our hands dirty by adding those personal touches that only humans can. Because at the end of the day, it’s about striking that sweet spot where tech meets human intuition.

So next time you give your older articles a makeover with the help of an AI tool, sprinkle in your insights and keep things relatable because people connect with people – not robots.

By combining AI’s production speed and human creativity, you’re setting yourself up for content that doesn’t just tick boxes for search engines but also hits home with every person reading it.

Dig deeper: Improving or removing content for SEO: How to do it the right way

SEO has come a long way from the days of keyword stuffing. Modern search engines like Google now rely on advanced natural language processing (NLP) to understand searches and match them to relevant content.

This article will explain key NLP concepts shaping modern SEO so you can better optimize your content. We’ll cover:

• How machines process human language as signals and noise, not words and concepts.
• The limitations of outdated latent semantic indexing (LSI) techniques.
• The growing role of entities – specifically named entity recognition – in search.
• Emerging NLP methods like neural matching and BERT go beyond keywords to understand user intent.
• New frontiers like large language models(LLMs) and retrieval-augmented generation (RAG).

How do machines understand language?

It’s helpful to begin by learning about how and why machines analyze and work with text that they receive as input.

When you press the “E” button on your keyboard, your computer doesn’t directly understand what “E” means. Instead, it sends a message to a low-level program, which instructs the computer on how to manipulate and process electrical signals coming from the keyboard. 

This program then translates the signal into actions the computer can understand, like displaying the letter “E” on the screen or performing other tasks related to that input.

This simplified explanation illustrates that computers work with numbers and signals, not with concepts like letters and words.

When it comes to NLP, the challenge is teaching these machines to understand, interpret, and generate human language, which is inherently nuanced and complex. 

Foundational techniques allow computers to start “understanding” text by recognizing patterns and relationships between these numerical representations of words. They include:

• Tokenization, where text is broken down into constituent parts (like words or phrases).
• Vectorization, where words are converted into numerical values.

The point is that algorithms, even highly advanced ones, don’t perceive words as concepts or language; they see them as signals and noise. Essentially, we’re changing the electronic charge of very expensive sand.

LSI keywords: Myths and realities

Latent semantic indexing (LSI) is a term thrown around a lot in SEO circles. The idea is that certain keywords or phrases are conceptually related to your main keyword, and including them in your content helps search engines understand your page better.

Simply put, LSI works like a library sorting system for text. Developed in the 1980s, it assists computers in grasping the connections between words and concepts across a bunch of documents. 

But the “bunch of documents” is not Google’s entire index. LSI was a technique designed to find similarities in a small group of documents that are similar to each other.

Here’s how it works: Let’s say you’re researching “climate change.” A basic keyword search might give you documents with “climate change” mentioned explicitly. 

But what about those valuable pieces discussing “global warming,” “carbon footprint,” or “greenhouse gases”? 

That’s where LSI comes in handy. It identifies those semantically related terms, ensuring you don’t miss out on relevant information even if the exact phrase isn’t used.

The thing is, Google isn’t using a 1980s library technique to rank content. They have more expensive equipment than that. 

Despite the common misconception, LSI keywords aren’t directly used in modern SEO or by search engines like Google. LSI is an outdated term, and Google doesn’t use something like a semantic index.

However, semantic understanding and other machine language techniques can be useful. This evolution has paved the way for more advanced NLP techniques at the core of how search engines analyze and interpret web content today.

So, let’s go beyond just keywords. We have machines that interpret language in peculiar ways, and we know Google uses techniques to align content with user queries. But what comes after the basic keyword match? 

That’s where entities, neural matching, and advanced NLP techniques in today’s search engines come into play.

Dig deeper: Entities, topics, keywords: Clarifying core semantic SEO concepts

The role of entities in search 

Entities are a cornerstone of NLP and a key focus for SEO. Google uses entities in two main ways:

• Knowledge graph entities: These are well-defined entities, like famous authors, historical events, landmarks, etc., that exist within Google’s Knowledge Graph. They’re easily identifiable and often come up in search results with rich snippets or knowledge panels.
• Lower-case entities: These are recognized by Google but aren’t prominent enough to have a dedicated spot in the Knowledge Graph. Google’s algorithms can still identify these entities, such as lesser-known names or specific concepts related to your content.

Understanding the “web of entities” is crucial. It helps us craft content that aligns with user goals and queries, making it more likely for our content to be deemed relevant by search engines.

Dig deeper: Entity SEO: The definitive guide

Understanding named entity recognition

Named entity recognition (NER) is an NLP technique that automatically identifies named entities in text and classifies them into predefined categories, such as names of people, organizations, and locations.

Let’s take the example: “Sara bought the Torment Vortex Corp. in 2016.” 

A human effortlessly recognizes: 

• “Sara” as a person. 
• “Torment Vortex Corp.” as a company.
• “2016” as a time. 

NER is a way to get systems to understand that context. 

There are different algorithms used in NER:

• Rule-based systems: Rely on handcrafted rules to identify entities based on patterns. If it looks like a date, it’s a date. If it looks like money, it’s money.
• Statistical models: These learn from a labeled dataset. Someone goes through and labels all of the Saras, Torment Vortex Corps, and the 2016s as their respective entity types. When new text shows up. Hopefully, other names, companies, and dates that fit similar patterns are labeled. Examples include Hidden Markov Models, Maximum Entropy Models, and Conditional Random Fields.
• Deep learning models: Recurrent neural networks, long short-term memory networks, and transformers have all been used for NER to capture complex patterns in text data.

Large, fast-moving search engines like Google likely use a combination of the above, letting them react to new entities as they enter the internet ecosystem. 

Here’s a simplified example using Python’s NTLK library for a rule-based approach:

import nltk

from nltk import ne_chunk, pos_tag

from nltk.tokenize import word_tokenize

nltk.download('maxent_ne_chunker')

nltk.download('words')

sentence = "Albert Einstein was born in Ulm, Germany in 1879."

# Tokenize and part-of-speech tagging

tokens = word_tokenize(sentence)

tags = pos_tag(tokens)

# Named entity recognition

entities = ne_chunk(tags)

print(entities)

For a more advanced approach using pre-trained models, you might turn to spaCy:

import spacy

# Load the pre-trained model

nlp = spacy.load("en_core_web_sm")

sentence = "Albert Einstein was born in Ulm, Germany in 1879."

# Process the text

doc = nlp(sentence)

# Iterate over the detected entities

for ent in doc.ents:

    print(ent.text, ent.label_)

These examples illustrate the basic and more advanced approaches to NER.

Starting with simple rule-based or statistical models can provide foundational insights while leveraging pre-trained deep learning models offers a pathway to more sophisticated and accurate entity recognition capabilities.

Entities in NLP, entities in SEO, and named entities in SEO

Entities are an NLP term that Google uses in Search in two ways. 

• Some entities exist in the knowledge graph (for example, see authors).
• There are lower-case entities recognized by Google but not yet given that distinction. (Google can tell names, even if they’re not famous people.) 

Understanding this web of entities can help us understand user goals with our content 

Neural matching, BERT, and other NLP techniques from Google

Google’s quest to understand the nuance of human language has led it to adopt several cutting-edge NLP techniques. 

Two of the most talked-about in recent years are neural matching and BERT. Let’s dive into what these are and how they revolutionize search.

Neural matching: Understanding beyond keywords

Imagine looking for “places to chill on a sunny day.” 

The old Google might have honed in on “places” and “sunny day,” possibly returning results for weather websites or outdoor gear shops. 

Enter neural matching – it’s like Google’s attempt to read between the lines, understanding that you’re probably looking for a park or a beach rather than today’s UV index.

BERT: Breaking down complex queries

BERT (Bidirectional Encoder Representations from Transformers) is another leap forward. If neural matching helps Google read between the lines, BERT helps it understand the whole story. 

BERT can process one word in relation to all the other words in a sentence rather than one by one in order. This means it can grasp each word’s context more accurately. The relationships and their order matter.

 “Best hotels with pools” and “great pools at hotels” might have subtle semantic differences: think about “Only he drove her to school today” vs. “he drove only her to school today.”

So, let’s think about this with regard to our previous, more primitive systems.

Machine learning works by taking large amounts of data, usually represented by tokens and vectors (numbers and relationships between those numbers), and iterating on that data to learn patterns. 

With techniques like neural matching and BERT, Google is no longer just looking at the direct match between the search query and keywords found on web pages. 

It’s trying to understand the intent behind the query and how different words relate to each other to provide results that truly meet the user’s needs. 

For example, a search for “cold head remedies” will understand the context of seeking treatment for symptoms related to a cold rather than literal “cold” or “head” topics.

The context in which words are used, and their relation to the topic matter significantly. This doesn’t necessarily mean keyword stuffing is dead, but the types of keywords to stuff are different. 

You shouldn’t just look at what is ranking, but related ideas, queries, and questions for completeness. Content that answers the query in a comprehensive, contextually relevant manner is favored. 

Understanding the user’s intent behind queries is more crucial than ever. Google’s advanced NLP techniques match content with the user’s intent, whether informational, navigational, transactional, or commercial. 

Optimizing content to meet these intents – by answering questions and providing guides, reviews, or product pages as appropriate – can improve search performance. 

But also understand how and why your niche would rank for that query intent.

A user looking for comparisons of cars is unlikely to want a biased view, but if you are willing to talk about information from users and be crucial and honest, you’re more likely to take that spot. 

Large language models (LLMs) and retrieval-augmented generation (RAG)

Moving beyond traditional NLP techniques, the digital landscape is now embracing large language models (LLMs) like GPT (Generative Pre-trained Transformer) and innovative approaches like retrieval-augmented generation (RAG). 

These technologies are setting new benchmarks in how machines understand and generate human language.

LLMs: Beyond basic understanding

LLMs like GPT are trained on vast datasets, encompassing a wide range of internet text. Their strength lies in their ability to predict the next word in a sentence based on the context provided by the words that precede it. This ability makes them incredibly versatile for generating human-like text across various topics and styles.

However, it’s crucial to remember that LLMs are not all-knowing oracles. They don’t access live internet data or possess an inherent understanding of facts. Instead, they generate responses based on patterns learned during training.
So, while they can produce remarkably coherent and contextually appropriate text, their outputs must be fact-checked, especially for accuracy and timeliness.

RAG: Enhancing accuracy with retrieval

This is where retrieval-augmented generation (RAG) comes into play. RAG combines the generative capabilities of LLMs with the precision of information retrieval. 

When an LLM generates a response, RAG intervenes by fetching relevant information from a database or the internet to verify or supplement the generated text. This process ensures that the final output is fluent, coherent, accurate, and informed by reliable data.

Applications in SEO

Understanding and leveraging these technologies can open up new avenues for content creation and optimization. 

• With LLMs, you can generate diverse and engaging content that resonates with readers and addresses their queries comprehensively. 
• RAG can further enhance this content by ensuring its factual accuracy and improving its credibility and value to the audience.

This is also what Search Generative Experience (SGE) is: RAG and LLMs together. It’s why “generated” results often skew close to ranking text and why SGE results may seem odd or cobbled together.

All this leads to content that tends toward mediocrity and reinforces biases and stereotypes. LLMs, trained on internet data, produce the median output of that data and then retrieve similarly generated data. This is what they call “enshittification.”

4 ways to use NLP techniques on your own content

Using NLP techniques on your own content involves leveraging the power of machine understanding to enhance your SEO strategy. Here’s how you can get started.

1. Identify key entities in your content

Utilize NLP tools to detect named entities within your content. This could include names of people, organizations, places, dates, and more.

Understanding the entities present can help you ensure your content is rich and informative, addressing the topics your audience cares about. This can help you include rich contextual links in your content.

2. Analyze user intent

Use NLP to classify the intent behind searches related to your content. 

Are users looking for information, aiming to make a purchase, or seeking a specific service? Tailoring your content to match these intents can significantly boost your SEO performance.

3. Improve readability and engagement

NLP tools can assess the readability of your content, suggesting optimizations to make it more accessible and engaging to your audience.

Simple language, clear structure, and focused messaging, informed by NLP analysis, can increase time spent on your site and reduce bounce rates. You can use the readability library and install it from pip.

4. Semantic analysis for content expansion

Beyond keyword density, semantic analysis can uncover related concepts and topics that you may not have included in your original content.

Integrating these related topics can make your content more comprehensive and improve its relevance to various search queries. You can use tools like TF:IDF, LDA and NLTK, Spacy, and Gensim.

Below are some scripts to get started:

Keyword and entity extraction with Python’s NLTK

import nltk

from nltk.tokenize import word_tokenize

from nltk.tag import pos_tag

from nltk.chunk import ne_chunk

nltk.download('punkt')

nltk.download('averaged_perceptron_tagger')

nltk.download('maxent_ne_chunker')

nltk.download('words')

sentence = "Google's AI algorithm BERT helps understand complex search queries."

# Tokenize and part-of-speech tagging

tokens = word_tokenize(sentence)

tags = pos_tag(tokens)

# Named entity recognition

entities = ne_chunk(tags)

print(entities)

Understanding User Intent with spaCy

import spacy

# Load English tokenizer, tagger, parser, NER, and word vectors

nlp = spacy.load("en_core_web_sm")

text = "How do I start with Python programming?"

# Process the text

doc = nlp(text)

# Entity recognition for quick topic identification

for entity in doc.ents:

    print(entity.text, entity.label_)

# Leveraging verbs and nouns to understand user intent

verbs = [token.lemma_ for token in doc if token.pos_ == "VERB"]

nouns = [token.lemma_ for token in doc if token.pos_ == "NOUN"]

print("Verbs:", verbs)

print("Nouns:", nouns)

Google is piloting branded Local Service Ads (LSA). Brands are automatically enrolled into this new ad type by default. To disable it, businesses can adjust the settings manually.

Branded LSAs explained. This new ad type allows brands to show up in search results when someone directly searches for a brand or business.

Why we care. While this ad type may offer the chance to display your ad when someone is actively searching for a competitor, allowing you to tap into their customer base, it’s essential to consider the possibility of paying for conversions that you might have naturally obtained anyway. This happens when users click on your ad, even when your organic listing is right below it in the search results, as clicking on the organic listing would not have incurred any cost.

First look. Branded LSAs were first flagged by Anthony Higman, CEO of online advertising agency Adsquire, who shared a screenshot on X:

Reassurance for advertisers. Expressing concern, Higman noted that brands currently lack the option to opt out of branded LSAs. Joy Hawkins, owner of the Local Search Forum, LocalU, and Sterling Sky, a Local SEO agency in Canada & the USA, shared this sentiment, confirming her inability to opt out of this ad type too. However, Google provided assurance that the ability for all brands to opt-out will be available in the near future.

Opting out. Ben Fisher, Co-Founder/Lead Consultant at Diamond Google Product Expert, explained on X that brands can opt out of branded LSA by going to settings, and turning off the Direct business search toggle, as demonstrated in the screenshot below:

If the option is not currently available, it’s important to note that branded LSAs are still in the early stages, and Google will be making this choice accessible to everyone in the near future.

What Google is saying. Google Ads liaison officer Ginny Marvin said on X:

• “This is an experiment that will be rolling out shortly. To be clear, the opt-out will be available for everyone. Customers will receive more information on this next week.”

Google expanded its generative AI features for automatically created assets (ACA) to all English language advertisers in the U.S. and UK.

Why we care. If you are an English-speaking advertiser in the U.S. or UK, you can now start utilizing generative AI to help create your ad campaigns, giving you access to a broader range of ideas instantly and significant time-saving benefits.

ACAs explained. Automatically created assets (ACAs) are a campaign-level setting that advertisers can opt into. By activating this setting, assets such as headlines and descriptions are automatically generated. These generated assets complement the ones provided by advertisers for their responsive search ads, enhancing the overall ad content.

Generative AI and ACAs. In some cases, generative AI is used to create assets that better match what people are searching for. This helps ensure that the generated content is relevant and aligns with users’ search queries.

How it works. Automatically created assets (ACAs) work in conjunction with responsive search ads by generating new content, like headlines and descriptions, based on the specific details of your ad. This includes factors such as your landing page, existing ads, and keywords.

When your ad is eligible to appear for a query, the responsive search ads consider both the pool of assets you’ve created and the automatically generated assets. The system then serves the combination predicted to perform the best.

What Google is saying. Google Ads liaison officer Ginny Marvin said on X:

• “Generative AI is used to improve the relevancy of assets to the search query and predicted performance of your Search ads in Search and Performance Max campaigns.”
• “ACA is meant to supplement, not replace, your existing headlines and descriptions.”

Deep dive. Read Google’s automatically created assets guide for more information.