What Is AI Training Data? Everything You Need to Know

A complete guide to AI training data: what it is, the main data types (text, image, video, audio, tabular), and why trusted, well-licensed data is the biggest bottleneck in AI development today.

When OpenAI launched ChatGPT in 2022, people were amazed by how naturally a Large Language Model (LLM) could interact with and respond to human questions. Since then, companies across every industry have rushed to develop and adopt AI systems. Today, AI is widely used for managing enterprise knowledge, answering customer inquiries, and generating marketing content. According to McKinsey's 2023 Global Survey on AI, one-third of respondents were already using generative AI regularly. By 2025, the majority of enterprises had begun experimenting with agentic workflows and AI tools.

However, with generative AI adoption accelerating, companies are facing one of their biggest challenges: a lack of trusted training data.

What Is AI Training Data?

To understand what AI training data is, it helps to look at how AI systems are trained.

First, companies collect and prepare data. This involves cleaning datasets, correcting errors, and removing personal information where necessary. The quality of this data directly affects the intelligence and performance of the AI model.

Next, businesses decide how to train their systems. Some use retrieval-augmented generation (RAG), which allows AI to retrieve information from a dedicated knowledge base, while others use fine-tuning to adapt models for specific tasks, terminology, or communication styles.

Finally, models are aligned and tested. Through guardrails and reinforcement learning from human feedback (RLHF), businesses ensure AI behaves reliably and responds appropriately.

This process highlights a simple reality: because AI data is fed into LLMs at the first and final stages to train and reinforce the model, its quality, structure, and form determine the model's development. If the data fed into a model is full of errors or outdated, the model may generate the wrong answer or action. In other words, AI is only as good as the data behind it.

Types of AI Training Data

Some common types of AI training data include text, images, videos, audio, and tabular data. Here's how each is typically used:

Text

Text is one of the most common types of AI training data. Documents, blogs, media content, websites, and creative writing all help LLMs develop better language responses and translate more accurately between languages.

Images and Videos

While text data helps build an LLM's language responses, images and video sharpen a model's visual capabilities: facial recognition and verification, action recognition, deepfake detection, and image or video generation in GenAI applications and workflows.

This also includes egocentric POV videos recorded while a person is carrying out everyday tasks, used to train ML models powering robotics and hardware infrastructure (such as robots, drones, production lines, and autonomous vehicles).

Audio

While more LLMs and AI models add conversational features that let people talk directly to them, audio has also become an important data source for both general-purpose LLMs and educational AI tools. Beyond human conversation, environmental sound recordings also help train AI to generate more realistic audio and video content.

Tabular Data

Organised into rows and columns, tabular data helps AI models analyse patterns and generate data-driven solutions and recommendations.

Most of the AI training data listed above can be found on an AI data marketplace such as Opendatabay. Because training AI and LLMs requires large, varied volumes of data, sellers rarely need to worry about whether their data is valuable. As long as they maintain well-organised data, the odds are good that some developer somewhere needs it. And if buyers can't find the exact dataset they're looking for, Opendatabay can also help source it directly.

The Bottleneck: Why Is It So Difficult to Get AI Training Data?

The data supply chain is struggling to keep up with demand, and it's increasingly filled with content lacking clear provenance, licensing terms, or ownership records. Companies risk training models on unauthorised data and facing copyright disputes, while AI-generated content recycled from other models often provides limited value for improving performance.

At the same time, regulation is tightening. Under the EU Artificial Intelligence Act, providers of general-purpose AI (GPAI) models must publish a public summary of the data used to train their models. New models placed on the EU market from August 2025 already carry this obligation; providers of models that were already on the market before then have until August 2027 to comply. Organisations that cannot demonstrate lawful data sourcing may face significant legal and financial risks.

Thus, to solve the problem, Opendatabay aims to make buying and selling data as easy as shopping online. Using Ontology and our AIP system, we verify data ownership, provenance, and licensing rights to generate a trust score that helps buyers assess risk and compliance. Our AI-ready datasets include text, images, audio, video, code, and agentic trajectories, all of which can be directly integrated into AI workflows.

Supported by Microsoft, Google, Innovate UK, and IBM, Opendatabay is committed to helping organisations buy trustworthy data and unlock new opportunities in the AI economy.

Browse our website and follow our blog to learn more about the AI data marketplace. In the next article, we'll cover what an AI data marketplace actually is and provide a comprehensive guide for both data sellers and buyers in 2026.