Training large language models has moved from a laboratory discipline into the mainstream of enterprise solution development. Developers today face not only technical challenges but also new legal boundaries, the expansion of open-source architectures, and pressure for transparency in data provenance. This article offers a comprehensive overview of what training an AI model entails today - with references to current cases, decisions, and technological developments.
Training Pipeline: What It Consists Of
Training a large language model (LLM) today typically involves these phases:
| Phase | Description |
|---|---|
| Data Collection | Gathering extensive text corpora (web, books, code, documentation) |
| Filtering and Tokenization | Removing noise, duplicates, tokenization for neural network input |
| Pre-training | Statistical learning of language structure |
| Fine-tuning | Tuning the model for a specific domain or communication style |
| Alignment | Adjusting outputs using feedback (e.g., RLHF) |
Architecturally, most teams today use efficient training frameworks like DeepSpeed, Axolotl, vLLM, or are moving to smaller specialized models (Mistral, Phi-3).
What Data Can Be Used for Training and Why It Matters
The choice of training data directly affects both the model's performance and legal standing. In June 2025, a U.S. federal court ruled in favor of Meta, which used pirated copies of books from the Library Genesis database when training the LLaMA model. According to Judge Vince Chhabria, this constituted transformative use (fair use), because the model did not copy texts verbatim but used them to learn language patterns (The Verge, 2025).
Simultaneously, Anthropic defended training its Claude model on scanned physical books purchased legally. Judge William Alsup compared AI training to the way humans learn to read and write: reading is not copying, but learning the principle (AP News, 2025).
