This document discusses AI-generated content and tools for content generation. It summarizes the capabilities of the Serpstat Test Lab tool set, which includes tools for article building, keyword extraction, FAQ generation, and ad generation using AI models like GPT-3. It also discusses some of Google's policies around AI-generated content and common problems with AI generation tools.

1. Ekaterina Hordiienko
AI in content: how
to keep it humane?

2. Welcome
Message Do machines have the ability to mimic human learning? Deep learning is a type of
machine learning and artificial intelligence that imitates the way humans gain certain
types of knowledge. In the same way as humans, machines are fed with observations
(data). Data is analyzed by the learning algorithm, which finds the patterns that most
closely match the observations.
This pattern is expressed in the model that was learned. A mathematical function
defines the relationship between the models. Without patterns, there can be no
learning — neither human nor machine.
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Lecturer
Ekaterina Hordiienko | Research Editor | Serpstat

3. Table of
Contents
How does GPT-3 system differ from
previous versions of the text generation
models?
01
02
03
04
05
06
07
08
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A few ways to use GPT-3-related services
in an SEO
A few more creative cases of GPT-3 and
GPT-NeoX usage
Our research on the genuine content
The Abilities of the Test Lab tool
set
What does Google think of
AI-generated content?
The most common problems of AI
generation tools
Serpstat Test Lab

4. How does GPT-3 system differ from previous
versions of the text generation models?
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5. A few ways to use GPT-3-related
models in an SEO: Getting started
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1. FAQs and microcontent for landing pages
2. Titles and descriptions
3. Content, also for placeholders
4. Build your own tools

6. A few more creative cases of GPT-3 and
GPT-NeoX usage
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To create a search engine
To create a CV
To create SQL-queries
As a Google Spreadsheets function
As a designer tool with Figma to create markups and layouts
For writing podcasts
To write and understand code
For data visualization and in Data-journalism
For products description in E-commerce
To detect low-quality content and as a writing assistant — to create a
better blog post

7. When to use
and not to
use the GPT-3
Generating ideas
to the topics to
add to your
content plan
Scaling your
business without
additional
resources on the
custom texts
To empower your
team with content
assistance
Rewriting
small pieces of
text
Writing
keyword-targeted
blog posts
Branded content
Useful YMYL
content
Writing full
blog posts

8. The GPT-3 and GPT-NeoX based tool set
Serpstat TestLab
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9. The Abilities of Test Lab tool
set: Article builder tool
A keyword list based on the topic discovery will assist
in creating the title for the article.
The Article generation tool will give you different
results each time you run it to help you improve the
article's structure. Doing so will save time when
creating SEO text and PRDs (Product requirements
document) for copywriters.
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10. The Abilities of Test Lab tool set: Article
builder tool
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The most common
use-cases:
To create technical requirements to
copywriter
To generate ideas for title or contents
To create blog posts and check
demand for future improvements

11. The Abilities of Test Lab tool set: Bag of Words
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The tool is similar to our previously released Keywords Extraction, but does not use AI. Due to this, the Bag of Words works
faster. The tool shows the most used keywords in the text with keywords frequency (the number of references in the text).
The most common use-cases:
To determine which phrases are missing and which are
excessive
To delve into the content and highlight the main topic
quickly
To create an infographic
To get an idea from competitors' content for own
keyword research before the article writing

12. The Abilities of Test Lab tool set: Keywords
Extraction
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Set up the appropriate settings for
extraction based on your task:
● number of words in a keyword;
● number of keywords to display;
● the level of relevance to the added
text — Maximal Margin Relevance
(MMR). It creates keywords based
on cosine similarity.
● remove stop keywords.
The most common
use-cases:
To observe your text with “Google’s eyes”

13. The Abilities of Test Lab tool set: Keyword patterns
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Keyword patterns is an alternative version of the Keyword clustering tool,
where keywords are grouped not by SERPs but by topic. You can process
up to 10K keywords at once. Results will contain the following data:
● Pattern — a keyword pattern, with an underline to replace;
● Count — how many times a pattern was found in the added list;
● Replaced — variations of words that can be added in a pattern.
The most common use-cases:
To create filters for an online store. For example, "apple _" pattern is used as a filter
category, and "pencil, airpods, ipad, iphone..." as filter options.
To expand a keywords list.
To generate text for meta tags, filters, tags etc.
To get trending topics from the list of trends (for news and media).

14. The Abilities of Test Lab tool set: Ad Generation
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After clicking on the "Generate ad" button, based on the found
examples of competitors and considering the phrases indicated
at the beginning, a customized ad is generated using Machine
Learning.
The most common use-cases:
To find ideas for ads

15. The Abilities of Test Lab tool set: FAQ Generation
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One more unique tool using Serpstat database from Keyword Research
section (Search Questions). Thanks to FAQ Generation, you can save time
searching for relevant topics to mention onsite. Additionally, you don't
have to search for answers on your own.
After generating the FAQ, you can export the results immediately,
marked up in a Schema.org-compliant format.
The most common use-cases:
To do keyword research.
To improve search engine visibility with FAQ snippet.
To enhance the interlinking of the site.
To conclude a content-plan.
In e-mail marketing.

16. What does Google
think of
AI-generated
content?
In cases where AI-generated content is intended to manipulate search
rankings rather than help users, Google may take action. Among the
examples are:
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The text is illogical to the reader, but contains relevant search terms.
Automated translations without human review or curation before
publication.
A text created by an automated process, such as a Markov chain.
An automated method uses synonymizing or obfuscation to produce
text.
A text that is generated from scraping Atom/RSS feeds or search results.
Content is stitched together or combined without adding enough value.
Source: Google Search Central documentation (Spam policies for Google web search)

17. Our research on the genuine content
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18. The most
common
problems of AI
generation tools
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Samuel H. Altman, CEO of
OpenAI and the former
president of Y Combinator

19. There will be no reduction in copywriting and marketing jobs. Content specialists will be
needed to understand how content fits into the larger marketing strategy, and to create,
manage, and optimize the content that GPT-3 writes.
By using AI tools to generate content, you are doing just that — creating content. Especially
when you're just starting out, it's very likely that your content will need some human touch,
whether it's editing or formatting. Despite becoming well-versed in your wants and needs,
you'll probably still have to tweak the content once in a while, so it sounds right.
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20. Ready to test
AI tools for
your SEO
tasks?
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21. Thank
you
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