Generative AI, GPT models, ChatGPT – the NLG market is developing extremely dynamically. Large language models enable automated content creation with just a few prompts and unstructured 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. This type of generative AI utilises nearly all the knowledge available online for text generation, which is why texts can be created quickly and cost-effectively with less information.
However, text-to-text often produces errors in the content, as they lack the necessary context, do not incorporate the latest developments or their models generate incorrect statements or duplicate content. Consequently, GPT models, ChatGPT and the like are not very suitable for business process automation and scaling, as human quality assurance is required for the generated texts.
With our Hybrid Natural Language Generation (NLG) solution, we are combining our data-based approach to automated text generation with the benefits of large language models. This allows companies to use dynamic and static content from GPT models within the content automation platform textengine.io, which is based on text models – so users get the best of both worlds.
Effectiveness booster through the use of unstructured data and automated creation of text models in a few minutes
Preconfigured text types for customised content in a wide variety of formats and channels
Use of world knowledge and different communicative styles combined with individual prompts and rewrites
Creative and search engine optimised text variant suggestions even with several tens of thousands or hundreds of thousands of texts
Better, faster and resource-efficient solutions for multilingual content projects through a high degree of scaling
100% correct and legally compliant content even with large amounts of text and a wide variety of output channels
With Hybrid NLG, the automated content generation process is based on unstructured data and automatically generated text models – significantly expediting the launch, reboot or scaling of online shops, marketplaces and comparable text projects. Even without uploading data sets, it is possible to use prompts as input for text automation. No matter your industry, you create automated and GPT-supported content and text models in the textengine.io.
Automatically create copy from entire keyword sets for a wide range of text types, including:
This process allows teams in content, SEO and marketing to concentrate on reviewing the generated texts and fine-tuning the content. You can realise a wide variety of content formats and complete text models within in a few minutes, so that you can quickly get into content automation.
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 expectations:
Your content production benefits from the use of freely accessible world knowledge, structured data, and data-based text models, providing the necessary framework. This ensures that the result is always 100% accurate, whilst also taking advantage of the benefits of large language models.
In short: With the assistance of text-to-text, you can tap all the opportunities offered by sophisticated, data-based NLG technology.
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 text-to-text solutions such as GPT and ChatGPT as well as comparable large language models that are based on the global knowledge available on the internet.
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 always pursued an approach based on uniquely configurable text models. This approach has the significant advantage that, after the initial setup, customers can achieve 100% content automation. 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.
For creative text generation, Retresco also features a GPT integration. This technology is part of the so-called 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.
By integrating large language models into its well-established, text model-based approach, Retresco has created a Hybrid Natural Language Generation solution to support all relevant text types of use cases and content and to combine the best of both NLG worlds. For more efficiency and a fast time-to-market, unstructured data can also be used to generate variant text suggestions and keyword sets. The process also offers major benefits for customers, including a great deal of flexibility and a rapid time to market for their content projects.
Hybrid Natural Language Generation combines text model-based content generation with the capabilities of GPT models. This allows customers to use dynamic and static GPT content within the template-based content automation platform textengine.io.
In this context, dynamic means that text variants and text models are generated automatically by GPT-3, GPT-4 and co. Content, SEO and Marketing Teams can choose from GPT suggestions when writing new content templates and texts and integrate them into their workflows. In this way, the team members always have the possibility to create large-scale content and text models fast and effectively.
More on our text automation platform: https://www.retresco.com/solutions/text-automation
Hybrid NLG can be used in any situation where text matters. GPT-based text variant suggestions and text models just need good prompts 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. At the same time, texts can be easily and automatically realigned and rewritten through customer-specific instructions in the self-service platform textengine.io. When creating new templates and texts, content managers can choose from GPT suggestions and insert them. For the text models, the correctness of the texts is ensured by fixed rules and conditions or hard-coded storyplots.
GPT is seamlessly integrated into the 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 functions and the template-based ones.
More on our text automation platform: https://www.retresco.com/solutions/text-automation
Prompts can be used for text creation and development of text models with the self-service platform textengine.io. A prompt is a sentence or individual keywords that are used as input for text automation. The textengine.io then attempts to analyse and understand the input in order to automatically generate a response. It is therefore important that the prompts are formulated in such a way that they are understandable for the AI. A good prompt contributes to text automation achieving good results. Therefore, it is essential that the prompt input is formulated clearly, precisely and concretely.
With the rewrite functionality of the content automation platform textengine.io, content can be automatically rewritten and varied. Rewrite is designed to transform existing content and enrich unstructured data, trim it to the desired tone and length, and optimise it for search engines. This allows manufacturer texts, raw data, product description attributes or SEO tags to be easily and effectively reformulated and targeted at different audiences on a large scale.
Through a simple data upload after a one-time configuration, customers benefit from unique content, with even duplicate content being automatically rewritten. At the same time, texts can be specifically enriched by entering the desired keywords. Overall, Rewrite is characterised by lower resource and process expenditure.
High-quality 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. In addition to customised instructions and prompts, predefined formats like headline, intro, message, paragraph and call-to-action are available. With the GPT integration, Retresco offers all the functions necessary to create high-quality, SEO-compliant descriptions quickly and easily.
The content automation platform textengine.io transforms the GPT output 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.
Hybrid NLG combines text-to-text through GPT models and text model-based content automation from Retresco. The decisive benefits are in the creative process, simplified content generation, time-saving automation, text variant suggestions without data upload and the associated cost efficiency. In an internal audit, we tested the conventional creation of text models with the setup based on Hybrid NLG and the past updates from a power-user perspective. In internal tests, Retresco was able to determine a time expenditure of just 223 minutes for the complete textualisation of a fully equipped text template. These time and cost savings are based on the fact that text suggestions and text models are easily and quickly trainable thanks to GPT integration and the AI assistance systems of Retresco – thus making them productively usable in a short time with an efficient setup. This allows a functional text frame to be set up automatically for each individual work step within the Text Model Creator in just a few minutes.
New, more sophisticated text-to-text technologies regularly come onto the market, based on the feedback provided for existing large language models and the newest developments in the field of generative AI. 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 text-to-text sector; depending on how large language models develop, it is certainly possible that Retresco will integrate further text-to-text 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.
The workflows for text creation in textengine.io are based on text models. They structure and organise the production of the various text types in ongoing operations – and enable simple and effective content workflows. A Textmodel Creator can be used as a clever helper, which creates fully functional text models within minutes. This AI assistant dynamically generates all the structural elements of a text model (messages, templates, text variants) necessary for content production. At the same time, customers can manually fine-tune each element (Human-in-the-loop). For this, only the thematic framework needs to be defined during the initial setup. With the Textmodel Creator, a wide range of data can be utilised to create text models.
Because large language models like GPT-3, GPT-4 and co. 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 may result in discrimination on the basis of gender, religion, race or ethnicity. For this reason, GPT-based texts should always be subjected to quality control by the content, SEO and marketing teams.
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 in the field of generative AI, and Retresco’s stated goal is to actively embody these values, both internally and externally.
Retresco has extensive expertise in the fields of generative AI, Natural Language Processing, Natural Language Generation, machine learning, linguistic modelling, data analysis, data transformation and customer success management. Natural Language Processing refers to the capture, processing, and automation of natural language - and is developed at the interface of linguistics, computer science, and artificial intelligence. With the help of Natural Language Processing and generative AI, a wide variety of language and text-based business processes can be automated.
Generative AI creates entirely new opportunities to digitalise and automate business processes, tools, and offerings. Customers are comprehensively advised and supported by Retresco for successful projects. Over the past 15 years, Retresco has successfully designed and implemented more than 250 such AI projects. Typically, the possibilities for collaboration are explored in an AI workshop.
Learn more: https://www.retresco.com/ai-projects