Training: The initial phase where the model learns from a large dataset. This involves feeding the model vast amounts of text data and using techniques like supervised or unsupervised learning to adjust the model's parameters.

Customization: This involves fine-tuning the pretrained model on specific datasets related to the intended application. Customization makes the model more accurate and relevant for particular tasks or industries.

Inferencing: The deployment phase where the trained and customized model is used to make predictions or generate outputs based on new inputs. This step is critical for real-time applications and user interactions.