Hybrid NLG

Create Content Faster with
Integrated GPT-3 Assistance

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Large language models like GPT-3 make it possible to generate content automatically with minimal instruction and little data. These models use nearly all the information available online to generate text, which is why they can create enormous volumes of copy despite very little data being entered. With this type of content generation process, copy can be created quickly and efficiently at the push of a button.

However, end-to-end text generation and GPT-3 often produce errors in the content, as they lack the necessary context, do not incorporate the latest developments or their models generate incorrect statements. Consequently, they are unsuitable for large-scale business process automation, as every text generated requires human quality assurance – eliminating any potential financial advantage.

With our Hybrid Natural Language Generation (NLG) solution, we are combining our previous data-based approach to automated text generation with the benefits of large language models such as GPT-3. This allows companies to use dynamic and static content from GPT-3 within the content automation platform textengine.io, which is based on text models – so users get the best of both worlds.

New Options for a Faster Time to Market

Reduce time to market with a greater range of automation options for creating text models

Greater Opportunities for Different Types of Text

Automatically create a variety of new types of text for a wide range of channels

Integrated World Knowledge and Automatic Style

Use the integrated world knowledge and different styles of communication without the need for time-consuming data modelling

Unique Content for SEO and Omni-Channel Usage

Our automated text variant suggestions are even more creative, and they optimise SEO targets

Generate Multi-Language Content Faster and More Easily

Better, faster and more comprehensive solutions for multi-language content projects

Legally Compliant, Flawless Usage of NLG

A broader range of options to intervene in the content generation parameters for 100% correct, legally compliant copy

Discover how our content automation platform textengine.io can help you quickly create versatile copy for a broad range of text types, channels and target groups.


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Create a broad spectrum of text types with Hybrid NLG...

With Hybrid NLG, the automated content generation process is based on combined text models – significantly expediting the launch, reboot or scaling of online shops, marketplaces and comparable text projects. No matter your industry, you can generate your text models automatically with GPT-3 on our platform textengine.io, which accepts both static and dynamic content.

Automatically create copy from entire keyword sets for a wide range of text types, including:

  • Content and SEO copy
  • Category and product descriptions
  • Marketplace copy
  • POI content
  • Targeted content for omni-channel retail

This process allows teams in content, SEO and marketing to concentrate on reviewing the generated text and fine-tuning the content. You can train entire text models within a very short period of time, so you can start automating your content more quickly.

...with the 100% certainty provided by our text automation processes

You will have access to all the opportunities that data-based content production has to offer. A major benefit of the AI assistance systems on our platform is that they allow you to design customised, versatile copy – you can even choose between a more businesslike or more emotional style. Hybrid NLG is designed to generate highly personalised and channel-specific copy along the entire digital value chain – quick, effective and targeted.

Our platform meets the highest standards of:

  • Tone, brand presentation and target group appeal
  • Language-specific semantics and grammar rules
  • Compliance

Data-based text models provide the necessary framework for your content production processes, so you can be sure that the results are always 100% correct, as well as benefiting from all the advantages of a large language model like GPT-3.

In short: With the assistance of end-to-end text generation, you can tap all the opportunities offered by sophisticated, data-based NLG technology.

Frequently asked questions about Hybrid NLG and the opportunities it offers

What is Natural Language Generation?

Machine generation of text – also known as Natural Language Generation (NLG) or automated text generation – is a subset of artificial intelligence. Data, or access to large volumes to data, is the most important prerequisite for creating automatically generated copy. Wherever large volumes of data are generated – in digital commerce, media, the stock market or in sports, business or weather reporting – NLG software can automatically convert that data into versatile, easy-to-read content. There are two primary technological movements in the market for Natural Language Generation: rules-based content automation based on text models, and end-to-end solutions such as GPT-3 and comparable large language models that are based on the global knowledge available on the internet.

What technological approach is Retresco pursuing in the field of Natural Language Generation?

Customers can use Retresco’s self-service platform, textengine.io, to create highly personalised copy in a wide range of different languages. textengine.io automatically converts data into natural language copy. Natural Language Generation is the underlying technology, whilst content generation is based on text models set up at the beginning of the process; they provide the framework for the content based on a set of rules.

Retresco has thus far pursued an approach based on manually created text models. Consequently, this template-based approach initially required an editor to set up the text models. One major benefit of this approach is that after initial setup, customers can generate content completely automatically. All copy generated is grammatically edited and verifiably flawless. This means that the system can produce legally verifiable statements – an essential requirement for many use cases.

Thanks to the integration of GPT-3, Retresco now also supports fast, user-friendly end-to-end text generation. GPT-3 is one of the large language models (LLMs) that use deep learning to collect essentially all the content available on the internet and teach themselves the required information. These models are capable of generating high-quality text automatically. The selling point of this NLG approach is than any type of content can be generated on demand and without the need for initial setup – which is why this type of technology is known as end-to-end text generation.

By integrating the large language model GPT-3 into its tried-and-tested template-based approach, Retresco has created a Hybrid Natural Language Generation solution to support all relevant types of use cases and content and to combine the best of both NLG worlds. The process also offers major benefits for customers, including a great deal of flexibility and a rapid time to market for their content projects.

How does Hybrid Natural Language Generation work at Retresco?

Hybrid Natural Language Generation combines text model-based content generation with the benefits of GPT-3 and its end-to-end technology. This allows customers to use dynamic and static content from GPT-3 within the template-based content automation platform textengine.io, without the need for external support.

In this context, dynamic refers to the hybrid text model in which components of content are automatically generated by GPT-3 or a comparable large language model, and teams in content, SEO and marketing initially set up further text fields. When creating new content templates, team members can select from GPT-3 suggestions and insert them into the text model in a static format. This way, team members always have the opportunity to monitor the text model and make sure that it is completely correct, and they also reap all the benefits of the rapid implementation times and creative advantages offered by a large language model.

What are the potential applications of Hybrid Natural Language Generation?

Hybrid NLG can be used in any situation where text matters. GPT-3-based text models just need good instructions and, in some cases, loosely or strictly structured keywords, and with that information alone, the technology can already generate the first drafts of a text or paragraph. When creating new templates, members of the content team can select from GPT-3 suggestions and insert them into the text model in a static format. The rules and conditions or hard-coded story plots they define will ensure that the copy generated is correct.

GPT-3 is seamlessly integrated into the self-service platform textengine.io, so teams in content, SEO and marketing can use and manage the entire text automation process themselves, without the need for them to write code or hire an external service provider. All content can be created and organised centrally – both the GPT-3 functions and the template-based ones.

What types of text does Hybrid Natural Language Generation support?

High-quality short, long and meta descriptions, title tags, and product and category texts are essential to ensure that search engines like Google will find your copy and generate traffic through organic searches. The quality and relevance of the content provided to search engines and customers are decisive factors here. With the integration of GPT-3, Retresco offers all the functions necessary to create high-quality, SEO-compliant descriptions quickly and easily.

The content automation platform textengine.io transforms the output from GPT-3 into the desired types of text. Customers benefit from entire keyword sets and automated descriptions for their online shops, marketplaces and other content projects. The copy generated by the process is completely unique, and this content can also be re-generated and updated at any time. Using data for relevant keywords is also a user-friendly option, and the keywords can be automatically integrated into the desired text types. Last but not least, the system can generate links and update them regularly

How much time can Hybrid Natural Language Generation save?

Hybrid NLG combines GPT-3’s end-to-end text generation with Retresco’s template-based content automation. The decisive benefits are in the creative process, simplified content generation, time-saving automation and the associated cost efficiency. In initial tests, Retresco was able to achieve time savings of up to 50% in the process of setting up and using text models during the onboarding phase (the first three months). These time savings are based on the fact that the text models can be trained more quickly and easily thanks to the integration of GPT-3 and Retresco’s AI assistance systems – meaning that they can be used productively at an earlier point in time.

Does Retresco support other large language models in addition to GPT-3?

New, more sophisticated end-to-end technologies regularly come onto the market, based on the feedback provided for existing large language models. Artificial intelligence helps us develop a better understanding of the world and produce more convincing copy as a result. The more knowledge collected from various internet sources and databases for data training and the more computing power available, the more sophisticated the content results will be. Consequently, Retresco keeps a watchful eye on the technological developments in the end-to-end sector; depending on how large language models develop, it is certainly possible that Retresco will integrate further end-to-end technologies into its self-service platform textengine.io in future. The decisive factor here is the expansion of our range of functions and the usability of our platform to help our customers achieve success more rapidly as they implement their text projects. Additionally, Retresco is developing its own solutions and AI assistance systems based on these technologies that are already integrated into the platform.

What ethical standards does Retresco adhere to in the context of end-to-end text generation?

Because large language models like GPT-3 are based on data that is freely available on the internet, the technology is not immune to absorbing implicit or explicit bias or negative statements and incorporating them into the automatically generated content. Consequently, Retresco cannot rule out that the copy automatically generated by GPT-3 may result in discrimination on the basis of gender, religion, race or ethnicity. For this reason, teams in content, SEO and marketing should always review the quality of the copy generated based on GPT-3.

When it comes to Retresco and the products and services it offers, the Berlin-based company sees itself as an AI company with a sense of social responsibility operating at the crossroads of technology, business and society. It is all the more important for our company to have a solid, well-founded identity and strong corporate values. In that sense, Retresco’s values of transparency, accountability, responsibility and commitment reflect our identity as a technology provider, and Retresco’s stated goal is to actively embody these values, both internally and externally.

What services does Retresco offer in the field of Natural Language Generation?

Retresco has extensive expertise in the fields of Natural Language Generation, machine learning, linguistic modelling, data analysis, data transformation and customer success management. Natural Language Generation (NLG) is a technology being developed at the crossroads of linguistics, computer science and artificial intelligence. Natural Language Generation is a particularly important branch of this industry, and it can be used to automate a wide range of language- and text-based business processes.

Retresco provides its customers with comprehensive advice on implementing successful Natural Language Generation projects, supports them in boosting demand and customer satisfaction – and in driving growth. Retresco works with customers across many different industries to help them restructure their content processes, develop semantic processes and make their websites and services just that little bit smarter.

Learn more: https://www.retresco.com/services

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Matthias Fertig