What is training?

Training is the process of teaching an AI model by showing it examples. This is how models learn patterns and gain their capabilities.

Training is when you feed an AI model large amounts of data and let it learn patterns. The model adjusts its internal parameters (weights) to get better at predicting or generating outputs.

[Think of it like]: Showing someone thousands of examples of cats so they learn to recognize cats.

1. ▸[Prepare data]: Collect and organize training examples
2. ▸[Initialize model]: Start with a model that has random or basic knowledge
3. ▸[Show examples]: Feed the model training data
4. ▸[Model learns]: The model adjusts its parameters to match patterns in the data
5. ▸[Evaluate]: Test the model to see how well it learned
6. ▸[Iterate]: Repeat until the model performs well

[Text]: Books, articles, websites, code repositories [Images]: Photos with labels describing what's in them [Audio]: Recordings with transcriptions [Structured data]: Databases, spreadsheets, formatted information

[Forward pass]: Model makes a prediction based on current knowledge [Loss calculation]: Compare prediction to correct answer, calculate error [Backward pass]: Adjust model parameters to reduce error [Repeat]: Do this millions or billions of times

[Time]: Can take days, weeks, or months depending on model size [Compute]: Requires powerful computers, usually with GPUs [Data]: Needs massive amounts of training data [Cost]: Very expensive—can cost millions of dollars for large models

[Pre-training]: Initial training on general data (what companies like OpenAI do)

• ▸Trains on vast, diverse datasets
• ▸Creates general-purpose capabilities
• ▸Happens once per model version

[Fine-tuning]: Additional training on specific data (what you might do)

• ▸Trains on your specific use case
• ▸Adapts model to your needs
• ▸Can be done multiple times

[Capabilities]: Training determines what the model can do [Quality]: Better training data and processes produce better models [Bias]: Training data influences model behavior and potential biases [Knowledge cutoff]: Models only know what was in their training data

[AI companies]: OpenAI, Anthropic, Google train large general-purpose models [Researchers]: Academic researchers train models for research [Companies]: Some companies fine-tune models for specific use cases [Open source]: Community trains and shares open-source models

[Data quality]: Need high-quality, diverse training data [Computational cost]: Extremely expensive [Time]: Takes a very long time [Bias]: Training data can introduce biases into models [Evaluation]: Hard to know when training is "done"

Most people don't train models from scratch. Instead, you:

• ▸[Use pre-trained models]: Leverage models trained by AI companies
• ▸[Fine-tune]: Adapt existing models to your needs
• ▸[Prompt]: Use good prompts to get desired behavior

Understanding training helps you appreciate what AI models can do and their limitations.