SEO with ChatGPT: Prompts and Examples

From:

SISTRIX Content Team

Modified: 24.09.2025

In this article we will critically inspect the capabilities and limits of AI generated texts in the context of SEO. We will show what good content must accomplish nowadays, which role content creators and experts will continue to play and how ChatGPT can be integrated into the editorial workflow in a useful way.

What good texts in SEO have to fulfil today

A text for websites is not just a pure vessel for information. To be found in search engines, it has to fulfil a concrete goal: users have to be picked up right where their question, problem or interest begins. This means that a good “SEO text” orients itself by the search intent. It does not only answer the question but should also score for your own goals and generate more sales, leads or reach within the target group.

Each search query represents a specific purpose. People who google “best bikes 2025” are not looking for general texts regarding bikes, but a comparison, review or recommendation. Texts that miss this intention do not offer any additional value – and have a hard time achieving good rankings.

EEAT as the quality benchmark

Google primarily values content that show expertise, experience, authority and trustworthiness (EEAT). This doesn’t only concern medical or legal topics. Trust also plays a part in everyday questions. Who recommends something? What source is behind the text? Is the person a specialist, an affected person or somebody who just summarises content without specific knowledge?

A good SEO text does not work in isolation. It’s part of a content marketing strategy that knows its target group and competitive environment. Only those who build up content that is regularly evaluated and improved can create sustainable visibility.

How ChatGPT creates texts for SEO and where its limits lie

ChatGPT is based on a so-called Large Language Model. The model was trained on huge amounts of text and can generate new content based on this, that is grammatically correct and oftentimes seems surprisingly coherent in regards to content. It analyses the patterns of language, not real knowledge. Exactly therein lies its biggest strength but simultaneously also its biggest weakness.

Speed and structure instead of depth

ChatGPT can deliver a text draft within seconds. This seemingly saves time with content production. Its ability to summarise existing content seems to be especially helpful for many users, as well as creating outlines or different variants of the same paragraph. For topics with a high denseness in information this can be a great foundation.

What is missing is real depth. ChatGPT doesn’t evaluate or “understand” information, it simply produces a linguistically plausible text based on statistical probabilities. This becomes particularly problematic because the exact training data is not public. People on the outside can scarcely check on which sources a text is based – or if the information within it is even correct. Those who are not well-versed oftentimes don’t recognise errors or gaps.

No replacement for editorial responsibility

It is especially discerning that ChatGPT “invents” content if it can’t deliver fitting information. These so-called hallucinations seem to be correct at first glance, but are based on facts that can’t be checked. Whoever uses AI generated content without checking it risks false statements – with consequences for trust, legal safety and SEO performance.

This is why editorial competency is needed when working with AI texts. Without the inspection of the content and detailed revisions the text remains too arbitrary and risks breaking Google’s “Helpful Content” principle, or in the worst case applicable law.

The limits of AI show themselves exactly at the point where SEO texts have to show relevance and reliability.

Which content will be sought-after in the future

If AI tools like ChatGPT or Google’s AI Overview can deliver generic texts within seconds, a central question poses itself: which content still has relevance at this point? The answer lies within the differentiation. The contents that are wanted are those that are not easily copied. Content that is based on real experience, offers insights or create additional value that a machine cannot generate.

1. Experience based content

Texts with personal experience or practical use offer a clear advantage: they are credible, comprehensible and unique. For example, if someone tests a product, tries out a strategy or uses some sort of service, they provide information that is actually helpful for others. This also corresponds to what Google is specifically promotes with the “Experience” addition in the EEAT model.

Example: An article with real user feedback of an SEO tool has a lot more relevancy than a purely theoretical overview.

2. Data, analyses, opinions

Being based on data also creates differentiation. Anyone who conducts their own evaluations, analyses industry trends or shares informed opinions separates themselves from the generic content. Especially within B2B markets or professional topics is this approach a strong leverage to build up authority. To stand out from the crowd is and will continue to be the highest goal of SEO.

The important thing is transparency: Which data was used, how where they evaluated, which conclusion does the author come to?

3. Competent classification

Google is paying stronger attention to who is creating content. An author that is already known, that has professional relevancy and proven competency can improve trust and rankings. Anonymous, purely generated content has increasing difficulty in gaining visibility.

SEO texts that want to persist in the future also have to fulfil more. They need substance, personality and context. At this point it is clear how important the role of writers and experts will continue to have.

4. Technical relevancy

Anyone writing about a topic should also understand it. Only this way can content be correctly contextualised, displayed practically oriented and reasonably assessed. AI can suggest phrasing but it does not know what’s actually important. Humans recognise connections, can evaluate developments and tailor content selectively to the target audience.

Example: A professional article about the Google Search Console requires not only a description of the features but also experience with the handling of the tool. Without this perspective the text would remain superficial.

5. Editorial responsibility

Content created with machines has to be examined, revised and cleared. This process encompasses the correctness of the content, as well as legal aspects such as copy right or product identification. Those who write for companies or brands have a responsibility – and it can’t be delegated to some AI. To have the ability of inspecting content, the proofreaders have to be thoroughly informed about this content themselves as well.

6. Cooperation between human and machine

AI tools are currently changing the editorial process. They take on annoying routines, deliver first text drafts or help you with the structure. The actual work begins afterwards: checking topics, honing content, contextualising statements, supplementing examples, ensuring quality. Writers are increasingly turning into content editors with AI competency.

Those using ChatGPT sensibly are not necessarily working less, but differently, with more focus on strategy, relevance and quality. Writers can save time with the outline, drafts and formulation thanks to AI but the research effort stays the same.

In Practice: using ChatGPT for SEO with SISTRIX data

ChatGPT can meaningfully support persons responsible in SEO and content teams – if you know how to properly put it to use. The key is the quality of the prompts and the data. If you give precise directions and research your own data, you will receive better structured and more relevant results. But: AI output is never final. Only with human revision, correction and addition can a text be developed that really works.

1. Analysing keyword clusters and deducing content formats

Goal: To deduce meaningful content formats and subject areas from the long list.

Example prompt:
“Here is a keyword list with search volume. Based on this, create a topic cluster and suggest fitting content formats per cluster (e.g. guidebooks, template download, FAQ, comparison).
While doing that, factor in whether the keyword hints at an informational, navigational or transactional intent.
[Insert keyword list]”

➡️ Result: You will receive a primary content structure, for example with topics like “Templates for Small Businesses”, “Invoice Tools in Word vs. Excel” or “Free vs. Paid-For Invoice Templates”.

2. Developing user questions for FAQs – from long tail keywords

Goal: Formulating user questions based on concrete keywords to construct content in a user-centric way.

Example prompt:
“Write typical user questions for the following long tail keywords. The goal is to answer them in an FAQ area of a guidebook article.
[Example keywords: template invoice small business free, excel invoice template, invoice freelancer template]”

➡️ Result:

• „How can I, as a small entrepreneur, use a free invoice template?“
  
• „Which advantages does an Excel template offer for invoices?“
  
• „What should you watch out for when dealing with invoices for freelancers?“

3. Using keyword lists as a base for semantic outlines

Goal: Developing a content-wise coherent outline from a main keyword that covers related terms.

Example prompt:
“Create an outline for a guidebook about the topic ‘invoice template for small entrepreneurs’. Use relevant terms from this keyword list, without keyword stuffing.
[Excerpt from the list: invoice small entrepreneur template, template invoice small business, invoice small entrepreneur template, template small entrepreneur pdf, template small business owner free]”

➡️ Result: An outline with an H2/H3 structure that covers different use cases and data formats.

4. Analysing SERP competition and deriving a differentiation approach

Goal: Recognising how strong competitors are for certain keywords and what one’s own content has to deliver.

Example prompt:
“Here is the visibility data for the domains A, B and C for certain keywords. Explain which keywords and topics could be worthwhile to tackle with one’s own content because the competitors do not have top 10 rankings.

➡️ Result: A primary estimation of which niches are less fought over and can be approached with original content.

5. Generating variants for content titles and meta descriptions

Goal: Producing multiple SERP optimised variants from a keyword text.

Example prompt:
“Create five different title tag variants for the keyword ‘write invoice template’. The goal is a high click rate.
Limit the length to 60 characters and avoid repetitions.”

➡️ Result:

• “Writing Invoices: Free Template for Word & PDF”
  
• “Invoice Template to Fill In – Download Now”
  
• “Easy & Legally Watertight: Template for Writing Invoices”

6. Avoiding keyword spam but keeping relevancy

Goal: ChatGPT should help to incorporate a keyword naturally into a text draft without overdoing it.

Example prompt:
“Write a short introduction (max. 80 words) for an article for the keyword ‘writing invoice template’.
Use the keyword once but focus on naturalness and readability.”

➡️ Result: A user-friendly introduction that semantically fits the topic – without spam attributes.

7. Multilingual content

Goal: Using ChatGPT to fill multilingual pages with relevant content.

Prompt: “Translate the following text into German and adapt it linguistically to the target group in the Swiss market: [Text]. Use the following keywords: ‘kostenlose rechnung vorlage, rechnung, beleg vorlage, rechnung kostenvoranschlag vorlage kostenlos, rechnung generator, eine rechnung erstellen’.”

➡️ Result: ChatGPT cannot only translate but also localise – meaning that it can adjust the tone and wording to a target group. This saves a lot of time and effort, especially for international SEO strategies. However, the rule still applies: every text should be checked subject-wise and culturally by native speakers.

ChatGPT is a tool – not a replacement for strategy and quality

ChatGPT can speed up processes, offer ideas and take on repetitive tasks. Nevertheless, SEO is more than just the amount of text written. Visibility doesn’t come from pure quantity, it is based on relevance, context and a thought-out content strategy. The content needs to be satisfying for the target group and lead to them finding the entire domain to be more trustworthy.

Without a clear target group, the planning of topics or understanding about search intent every text simply remains one of a million. AI tools such as ChatGPT are helpful to create content fast – but only when they are part of a strict editorial process. Those that plan content, review and continuously optimise it can profit from AI. Those that rely on the automation don’t produce quality, but spam.

AI tools in practice: the SISTRIX Content Assistant AI

A tool that can usefully support the editorial process is the SISTRIX Content Assistant AI. It connects the AI functions from GPT-4 with the SEO data from SISTRIX and helps display content in a structurally coherent and data-based way.

The Content Assistant can, for example, offer fitting keywords, analyse the competition, suggest outlines and generate first text components upon request. Every suggestion is based on real SEO data – such as the search volume or visibility of particular terms. This doesn’t just make the content grammatically correct but also strategically useful.

The Content Assistant working together with ChatGPT is a good example of how AI tools can ease the workflow without replacing editorial responsibility. The actual quality is still coming from human evaluation, addition and categorisation.