What are the three broad steps in the lifecycle of Al for Large Language Models?

 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.

Reference: "Training is the foundational step where the AI model learns from data." (DeepMind,

2018)

 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.

Reference: "Customization tailors the AI model to specific tasks or datasets." (IBM Research, 2021)

 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.

Reference: "Inferencing is where AI models are applied to new data to generate insights." (Google AI,

2019)