Start with the building blocks.

A tokenizer turns text into smaller units called tokens. A token may represent a whole word, part of a word, punctuation, or another piece of text. The exact split depends on the tokenizer and the input.

A language model works with numeric representations of these tokens. When it generates text, it predicts a next token from the preceding context, then continues from the extended sequence.

Local AI usually means running a model on your own device or infrastructure instead of sending each request to a hosted model service. For example, a laptop might run a downloaded model to help summarize a document.

The model still needs computing resources. Memory, model size, and hardware affect what can run and how quickly it responds. A smaller model may be easier to run, but you still need to test whether it handles your task well.

Begin by defining what a good result means for your task. A summary might need to preserve key facts without adding claims. A classifier might need to choose the correct category, including for unusual examples.

Build a small test set with ordinary cases, difficult cases, and inputs where the right response is to acknowledge missing information. Keep some examples separate from the ones you use while improving the prototype.