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Choosing what to write on, in an AI-first world

This page provides practical advice for SEOs and site owners on selecting inputs (keywords, titles, etc.) for Byword that will maximize your results.

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Volume & difficulty

If you read the previous page on how AI is changing SEO, you’ll understand that tools like Byword open up possibilities to rank on search terms that are too low-volume to make sense when using human writers.

When doing keyword research for AI content, be far less sensitive to volume estimates than you would normally be.

It’s completely fine for a search term to have just one or two hundred searches a month. Even if that only translates to a couple of clicks a day, that quickly adds up when multiplied by hundreds or thousands of articles.

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Formats & coding

When analyzing search volume reports, it can often be beneficial to practice something called inductive coding. The basics of this approach are:

  1. Scan a search report (from a tool like Ahrefs or Semrush) for keywords that differ from one another in one constituent part. Examples of such keywords could be:

    • can cats eat carrots

    • can cats eat chicken

  2. Build a coded keyword structure from these keywords. In the example above, this would be can cats eat {food}.

  3. Populate a list of items that can be fed into your coding. For our example, this would be a list of foods which would make sense in the context of an article about cats eating that food.

  4. Build out all the keywords/titles in Excel or Google Sheets, and pass that into Byword.

This approach is particularly powerful with AI content production because tools like Byword can easily process hundreds of articles with this pattern. This allows you to build topical authority and produce content that would take weeks or months for human writers to create.

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Content type considerations

If you’ve used Byword already, you’ll notice that it performs differently with different types of content:

Byword excels with:

  • Factual, informative, educational content

  • Question-based keywords

  • Topics where the searcher is clearly trying to find specific information

Byword may struggle with:

  • Non-factual content

  • Opinion pieces

  • Reviews

  • Content requiring a high degree of subjectivity

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Understanding training data limitations

Byword’s algorithms are built upon language model prompts, which means its knowledge is limited to the knowledge of those models based on their training data.

This creates two notable limitations:

  1. Recency challenges: It can struggle when writing about very recent events or phenomena, particularly events that have occurred within the last year.

  2. Content depth correlation: The more that has been written historically about a topic, the better Byword will be able to write about it. For example, Byword is unlikely to produce high-quality content about your local neighborhood sports team, but can write excellent content about well-known global sports teams.

Be mindful of these limitations when selecting topics. For very recent events or highly specialized local topics, you may need to provide more context in your inputs or edit the content more heavily after generation.

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