Inside an LLM: from training to answering your questions

Large language models can feel complex, but the core workflow can be understood as two major phases: training and inference.

During training, text is tokenized, converted into embeddings, processed through transformer layers, and optimized so the model can learn patterns in language and context. During inference, the model receives a prompt, analyzes context, predicts the next likely token, and continues generating a response.

This type of educational view helps business and technology teams understand how LLM applications work behind the scenes and why prompt quality, context, retrieval, and governance matter for enterprise AI.