An AI Writing Articles: 8 Common Pitfalls to Tackle

AI writing articles has revolutionized the way that content is created and distributed.

More and more, artificial intelligence (AI) is being used to automate mundane tasks such as writing articles, blog posts, and even books.

However, there are some potential pitfalls to avoid when using AI in writing articles.

In this article, we'll explore 8 common pitfalls to watch out for when using AI for writing articles.

What is AI Writing?

AI Writing, also known as automated or machine writing, refers to the use of artificial intelligence and natural language processing (NLP) algorithms to generate written content without human intervention.

This technology can be used to create a wide range of content, including blog posts, news articles, marketing copy, product descriptions, and even creative writing, such as poetry and fiction.

AI Writing is becoming increasingly popular due to its ability to produce high-quality content quickly and efficiently.

And with Google now clearly stating it's not against their guidelines, the road is clear for all of us SEO professionals as well.

Benefits of AI Writing

AI writing can provide benefits such as increased efficiency and productivity, improved consistency and accuracy, and the ability to generate large volumes of content quickly.

Additionally, AI writing can help reduce costs associated with content creation and provide access to new types of content, such as personalized or dynamic content.

Most think of programs that generate content automatically on a variety of topics by leverage the advancement within use natural language processing and machine learning algorithms to algorithmically create new writing samples.
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But AI writing can be more than generating of new text. It can also cover the assistance of creating better texts. Like when using Grammerly or Hemminingway apps.

If you review this video, you will see my colleague Torbjørn demonstrates how quickly you can generate high-quality writing by utilizing AI.

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In summary, there are these benefits to AI writing:

• Increased efficiency and productivity: AI writing can generate content quickly and automatically, freeing up time for humans to focus on more complex tasks.
• Improved consistency and accuracy: AI writing can help ensure that written content is consistent and error-free, reducing the risk of mistakes and improving overall quality.
• Cost savings: Automating the writing process with AI can reduce the costs associated with human writers, such as salaries, benefits, and training.
• Access to new types of content: AI writing can create personalized or dynamic content that would be difficult or time-consuming for humans to produce.
• Scalability: AI writing can generate large volumes of content quickly, making it a powerful tool for companies that need to produce a high volume of content.
• Speed and responsiveness: AI writing can quickly generate content in response to changing events or trends, making it a useful tool for time-sensitive content creation.

The 8 Common Pitfalls to Tackle

Even with AI being so great at assisting the writing process, there are also some things you need to keep in mind when using AI writing. Let's review some of them.

1. Incorporating biases from the training data
2. Lack of originality or creativity in the writing output
3. Inaccurate representation of facts or data
4. Difficulty in handling complex writing tasks, such as writing legal or scientific documents
5. Reliance on pre-existing templates or patterns rather than generating unique content
6. Misaligned AI with Human Writing Style
7. Lack of personal touch or human connection in the writing output
8. Difficulty with understanding context or sarcasm

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1. Incorporating biases from the training data

Incorporating biases from the training data could be another common pitfall of AI writing that could be added to the list.

AI writing models may learn biases from the data they are trained on, which could result in perpetuating stereotypes, prejudices, or other forms of discrimination.

Therefore, it is crucial to ensure that the training data used for AI writing models are diverse, unbiased, and representative of the desired writing style and audience.

An example of this could be the following.

Let's say an AI writing model is trained on a large dataset of movie reviews, where most of the reviewers are male.

The model may learn to associate certain adjectives or descriptive words with male-led movies or male actors, even if those words are not inherently gender-specific.

For example, the model may learn to associate words like "action-packed" or "intense" with movies that have male leads, and words like "emotional" or "romantic" with movies that have female leads.

If this biased model is used to generate movie reviews for a broader audience, it may perpetuate gender stereotypes or inaccurately represent movies with diverse casts or storylines.

For instance, the model may generate a review for a female-led action movie that describes it as "surprisingly good for a chick flick," which could be offensive or off-putting to readers.

To avoid this type of bias in AI writing, it's important to use AI writers with algorithms with diverse and representative training dataset that reflects the desired tone and style of the writing output, as well as regular testing and validation of the model's results.

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2. Lack of originality or creativity in the writing output

This is not only a pitfall but also one of the objections I meet most often when discussing the capabilities of AI writing.

Since AI writing models are trained on existing data and patterns, they may generate content that closely mimics existing templates or styles, resulting in less unique or creative writing.

This can be especially problematic for creative writing tasks, such as fiction, poetry, or advertising copy, where originality and creativity are highly valued.

But, as the underlying algorithm and language models get more and more advanced, they are also capable of being more and more seemly original and creative.

And we still recommend seeing everything that you create with AI as a draft, that you can then edit and make better.

Something that struck me was being aligned with Microsoft CEO Satya Nadella that speaks of the AI creating drafts for humans to oversee in this interview with The Wall Street Journal.

If you don't want to watch it all, jump to 5:30.

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So humans work alongside AI to add personal touches or unique elements to the writing and to ensure that the output aligns with the desired tone and style.

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3. Inaccurate representation of facts or data

One of the biggest pitfalls of AI writing is inaccurate representation of facts or data that can impact the credibility and accuracy of the writing output.

AI writing models may generate content based on incomplete or biased information, resulting in factual errors or misrepresentations of data. This can be especially problematic for technical or scientific writing tasks, where accuracy and precision are crucial.

It may be that the algorithm has not been trained on recent data, and therefore only knows facts and events that occurred up until the date of its training data set. For example, ChatGPT only know about events that happened up until the end of the fourth quarter of 2021.

This has also been one of the great challenges that Google and Microsoft as they try to get AI into their search engines.

In its very first demo, Google Bard made an inaccurate representation of facts or data when it claimed that the James Webb Space Telescope had taken the very first pictures of a planet outside of our own solar system.

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Astronomers pointed out that the first image of an exoplanet was taken in 2004, and Google was criticized for the error.

This highlights a major problem for AI chatbots like Bard and ChatGPT, as they tend to confidently state incorrect information as fact, due to their probabilistic nature and reliance on analyzing patterns in text rather than querying a database of proven facts.

To avoid this pitfall, it's important to provide the AI model with accurate and reliable sources of information, to fact-check and verify the output regularly, and to provide context and explanations where necessary.

Additionally, human oversight can be valuable in ensuring the accuracy of the writing and in catching any errors or inaccuracies that the AI model may have missed.

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4. Difficulty in handling complex writing tasks, such as writing legal or scientific documents

When working with legal documents or similar complex writing tasks, AI writing often struggles to handle the nuances of these types of documents.

Due to the technical language, precise wording, and intricate details required in such documents, AI writing models may struggle to produce accurate and comprehensive content.

While AI writing tools can be useful in generating a base document or section of a document, they often require extensive human input to ensure accuracy and completeness, particularly in understanding context and lengthier documents.

Therefore, while AI can assist with writing these types of documents, it currently cannot replace the expertise and judgment of human writers and editors.

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5. Reliance on pre-existing templates or patterns rather than generating unique content

Using AI writing tools can be a great way to streamline the writing process and create consistent, standardized output.

However, the reliance on templates and patterns can lead to a lack of originality and creativity in the writing. This can result in a robotic, formulaic, and uninspired writing style that fails to capture the reader's interest.

For people looking to get the most out of an AI writing tool, it's important to provide the tool with a wide variety of examples, writing prompts, and data sets to draw from. This will help the AI tool generate more unique, interesting writing.

For inspiration on some prompts to use, you can see our list of good prompts for our SEO.ai platform.

Or see how we in this video demonstrate great usage of prompts to generate high-quality text.

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In general, it's useful to have human editors and writers assess the writing output and provide feedback to help refine the tool's creativity.

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6. Misaligned AI with Human Writing Style

The AI writing generated may not always match the style, tone, or voice of the human writer, leading to inconsistency in the writing output.

For example, an AI writing model may produce a formal, academic style of writing, while the human writer may prefer a more conversational or casual style.

To address this issue, it's important to train the AI writing model on various writing styles, preferences, and examples that match the desired tone and voice.

In our platform, this can be controlled by selecting the 'Tone of Voice' or providing guidance in the prompts of the desired writing style.

Also, the AI looks up in the document to see what style has been used so far and then reuses the same style and formatting.

Communication and collaboration between the AI writing model and human writers are essential to produce high-quality and consistent writing output.

Again, having humans review and edit the writing output to ensure that it aligns with the intended style and tone is always a good practice.

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7. Lack of personal touch or human connection in the writing output

Another common pitfall of AI writing is its inability to provide reliable examples, links to relevant content, and adherence to the E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) guidelines that Google recommends and ranks elements on.

This lack of support and credibility in the writing output can lead to a decreased level of user engagement and a negative impact on search engine rankings.

This is something that will for sure evolve, and we see many hacks that try to get around this.

The best approach for addressing the lack of personal touch or human connection in the writing output of AI is to see the generated content as a first draft.

After the initial draft is generated, human writers can review the content and add relevant elements such as examples, personal experiences, and links to additional content.

This is essentially how I do myself to create great pieces of content like the one you are reading now.

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8. Difficulty with understanding context or sarcasm

AI writing can sometimes struggle with grasping context or sarcasm.

This is due to the fact that AI writing models are based on patterns and probabilities rather than a true understanding of language and social cues.

As a result, they may misinterpret the intended meaning of a message or fail to pick up on subtle nuances that are important for effective communication.

Examples of difficulty with understanding context or sarcasm in AI writing may include:

• A chatbot programmed to answer customer service inquiries may respond inappropriately to a customer who uses sarcasm or humor in their message, mistaking the tone and providing an unsuitable response.
• An AI writing tool may struggle to identify the intended meaning of a sentence with multiple possible interpretations, leading to an inaccurate or confusing output.
• An automated news article may fail to properly contextualize a breaking story, leading to inaccurate or misleading reporting.
• A language model may struggle to recognize cultural nuances or regional dialects, resulting in language use that is stilted or awkward.

To avoid this issue, it is important to provide clear and unambiguous prompts.

And then use AI writing tools that utilize as advanced algorithms as possible with as broad and diverse training data sets as possible.

What types of content are best suited for AI writing?

Here are some different types of content that AI Writing can produce:

• News articles
• Product descriptions
• Blog posts
• Social media posts
• Email templates
• Business reports
• Landing page copy
• Press releases
• Essays
• Product reviews
• Speeches or presentations
• Technical manuals
• Legal documents
• Fictional stories
• Academic papers
• Poetry
• Ad copy
• Video scripts
• Chatbot dialogues
• Job descriptions
• Recipes

When it comes to the types of content best suited for AI writing, there are certain areas that may be more susceptible to falling into one of the pitfalls mentioned earlier, such as difficulty understanding context or sarcasm.

For example, AI writing tools can struggle to accurately interpret sarcasm or humor in social media posts, chatbot dialogues, and press releases, leading to inappropriate or confusing responses.

AI writing tools can also struggle to accurately interpret multiple meanings of a sentence in emails, product reviews, and academic papers, leading to inaccurate or misleading output.

Additionally, AI writing tools can struggle to recognize cultural nuances or regional dialects, resulting in language use that is stilted or awkward in speeches, presentations, and fictional stories.

Also, using classic AI writers for creating legal or scientific documents, academic papers or technical manuals can often be more difficult than other types of writing, due to the higher potential for falling into one of the pitfalls mentioned earlier.

But with technology and development on our side, even these pitfalls are gradually being erased as AI improves.