AI Pirates
DE | EN
AI Pirates
DE | EN
concept

Trainingsdaten

AI Basics

// Description

Training Data is the data on which an AI model is trained — it fundamentally determines what the model can and cannot do. LLMs like GPT-5.2 were trained on trillions of tokens: web pages, books, code, scientific papers, and more. The quality and composition of training data determines the model's capabilities.

The principle "Garbage In, Garbage Out" applies especially to AI: biased training data leads to biased models, missing domain data to knowledge gaps, and outdated data to outdated answers. That's why OpenAI, Anthropic, and Google invest hundreds of millions in curating high-quality training data.

For fine-tuning custom models, training data is key: just 50–100 high-quality examples can significantly improve a model. Creating good training data — collection, cleaning, labeling, quality control — is often the most labor-intensive part of an AI project, but also the most important.

Legal aspects: using copyrighted data for AI training is legally disputed (NYT vs. OpenAI, Getty vs. Stability AI). Companies should use licensed data, proprietary data, or synthetic data. The EU AI Act requires transparency about training data.

// Use Cases

  • Fine-tuning with proprietary company data
  • Data curation for AI projects
  • Synthetic data generation
  • Bias detection in training data
  • Data labeling & annotation
  • Dataset quality control
  • EU AI Act compliance
  • Domain-specific model improvement
// AI Pirates Assessment

Data quality > data quantity. For our fine-tuning projects, we invest more time curating good training data than in the actual training. 100 perfect examples beat 10,000 mediocre ones.

// Frequently Asked Questions

What is training data in AI?
Training data is the data on which an AI model is trained. LLMs are trained on trillions of tokens from web pages, books, and code. For fine-tuning custom models, as few as 50–100 high-quality examples often suffice.
Why is training data so important?
Training data fundamentally determines what a model can do: its strengths, weaknesses, biases, and knowledge limits. 'Garbage In, Garbage Out' — data quality limits model quality. Good training data matters more than a bigger model architecture.
Are there legal issues with training data?
Yes — using copyrighted data for AI training is legally disputed. Several lawsuits are ongoing (e.g., NYT vs. OpenAI). The EU AI Act requires transparency about training data. Companies should use licensed or proprietary data.

// Related Entries

Fine-Tuning Concept Neuronales Netz Concept Foundation Model Concept Halluzination Concept RLHF (Reinforcement Learning from Human Feedback) Concept

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