Artificial intelligence generative data in 2023. importance and 7 methods

As we witness the digital transformation of industries, generative AI is rapidly carving out a niche in the global AI market (Figure 1). It drives the creation of unique, high-quality content, mimics human language, designs innovative product prototypes, and even creates music.

However, unlocking the true potential of generative AI requires massive, diverse, and relevant data to train its models. This requirement challenges developers and business leaders alike, as collecting and preparing this data can be quite difficult.

This article explores generative data for artificial intelligence, its importance, and some methods for collecting relevant training data.

Figure 1: Adoption of generative AI

Source: Statista

What is generative AI data?

Artificial intelligence generative data refers to the vast amount of information used to train large language models. This data may include text, images, audio or video. Models learn patterns from this data, enabling them to generate new content that matches the complexity, style, and structure of the input data.

The Importance of Personal Data in Generative AI

Since the launch of OpenAI’s chatGPT, generative AI technologies have taken the tech world by storm. Business leaders are optimistic about the application of generative artificial intelligence in various industries (Figure 2).

A key aspect of the success of generative AI is its ability to offer contextually accurate and relevant output. To achieve this, the quality of the input data is very important. Private data, which is specific, customized and often proprietary, can significantly improve the performance of artificial intelligence generating models.

For example, Bloomberg developed BloombergGPT1:, a language model trained on their private financial data. This model outperformed generic models on finance-related tasks, demonstrating how targeted, industry-specific data can create a competitive advantage in the generative AI space.

Figure 2: Generative use cases of artificial intelligence

An illustration showing the different use cases of generative AI in different industries.  The picture shows that as generative AI applications grow, so will the need for generative AI data.
Source: Gartner

7 Data Collection Methods for Generative AI

When training large language models (LLMs), data collection is often the first hurdle. Below are some methods that developers can use:

1. Crowdsourcing

Crowdsourcing involves collecting data from a large group of people, usually over the Internet. This method can provide diverse, high-quality data. Imagine training a conversational AI model. You can collect speech data from users around the world, enabling the model to understand and generate dialogue in different languages ​​and styles.

However, crowdsourcing requires the development of an online platform that helps the company hire and manage the data gathering crowd. Working with a crowdsourcing service provider can be a more efficient way to use this approach to produce quality datasets for generative AI training.


Clickworker focuses on AI data generation and database preparation through a crowdsourcing platform. The company’s global team of more than 4.5 million employees helps 4 of the 5 US tech giants with their data needs. Clickworker’s scalable data services can help train and refine complex generative AI models with human-generated data.

2. Web crawling and scraping

Web crawling and scraping involves the automatic extraction of data from the Internet. For example, a generative AI model focused on news generation might use a crawler to collect articles from various news sites.

You can also check out our list of data-driven web scraping and crawling tools to find the best option for your business.

3. Creation of synthetic data

With the advent of powerful generative AI models, generating synthetic data is receiving increasing attention. In this approach, one generative AI model generates synthetic data to train another. For example, a generative AI model can create fictional customer interactions to train a customer service AI model. This approach can provide a large amount of relevant, diverse data without violating privacy rights.

Generative adversarial networks (GAN) can also be used to generate synthetic data. Click here to read about it.

4. Public Data Collections

Many organizations and individuals make datasets publicly available for research and development purposes, and these datasets can be used to train generative AI systems. These may include data sets:

  • Text: These are often used to make large language models such as GPT-3
  • Images: These datasets are commonly used to train text-to-image models such as Dall-e by OpenAI.
  • Audio: This data is typically used for tasks such as speech synthesis, music creation, or sound effects creation. A famous example is WaveNet by DeepMind.
  • Video. Generative AI systems that use video data are typically focused on tasks such as video synthesis, video prediction, or video-to-video translation.

Some examples of public data sets include:

  • Wikipedia is removing the text
  • For ImageNet images
  • LibriSpeech for voice
  • Books
  • News articles
  • Scientific journals

5. User Generated Content

Platforms such as social media sites, blogs, and forums are full of user-generated content that can be used as training data, subject to appropriate privacy and usage considerations. However, popular platforms like Reddit2: no longer provide free data to companies that make generative AI tools.

6. Adding data

Existing data can be modified or combined to create new data. This approach is called data augmentation and can be used to prepare datasets for training generative AI models. For example, images can be rotated, scaled, or otherwise transformed, while textual data can be synthesized by replacing, deleting, or rearranging words.

Studies (Figure 3) demonstrate the use of generative adversarial networks (GAN) to augment brain CT scan data.

Figure 3: Data augmentation using CycleGAN

Sample CT scan images.  CycleGAN was used for data augmentation to generate generative AI data.
Source: Nature

7. Customer Data

Proprietary data, such as customer call logs, can also be used to train large language models, particularly for customer service-related tasks such as generating automated responses, analyzing sentiment or recognizing intent. However, there are some important factors to consider when using this data:

  1. Transcription. Call logs, usually audio, need to be transcribed to text for training GPT-3 or GPT-4 text models.
  2. Privacy. Ensure that call logs are anonymized and comply with privacy laws and regulations, which may require express customer consent.
  3. bias. Call logs may contain biases that may affect the model’s performance during different call types or times.
  4. Data cleaning. Call logs require cleaning to remove noise such as irrelevant chatter, background noise, or transcription errors.


The importance of high-quality data cannot be overstated for the development of generative AI systems. The right data can greatly increase the effectiveness of a model, driving innovation and offering a competitive advantage in the market.

By exploring the data collection methods outlined in this article, developers and business leaders can navigate the complexities of AI generative data.

As generative AI continues to evolve, the focus on data will only intensify. Therefore, it is important to stay informed and adapt, ensuring that the AI ​​models you generate are not only data-rich, but also intelligent.

To learn more about AI data collection, download our free data collection white paper:

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Further reading

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  1. (March 30, 2023) “Introducing BloombergGPT, Bloomberg’s 50 billion-parameter large language model, targeted for finance from scratch.” Bloomberg: Signed in. May 16, 2023
  2. (April 18, 2023). “Building a Healthy Ecosystem for Reddit Data and Reddit Data API Access.” Reddit: Signed in. May 16, 2023

Shehmir Javaid is an Industry Analyst at AIMultiple. He has a background in logistics and supply chain management research and enjoys learning about innovative technologies and sustainability. He completed his MSc in Logistics and Operations Management from Cardiff University, UK and his BA in International Business Management from Cardiff Metropolitan University, UK.

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