How LLMs Work: A Practical Explainer for Product Managers Building AI Features

A guide published on DEV Community breaks down how Large Language Models function, targeting product managers who work on AI-powered products. The article explains that LLMs process text by converting it into tokens, transforming those tokens into numerical embeddings, and then predicting the next token in a sequence using a Transformer architecture. It highlights why understanding tokenization matters beyond theory — token volume directly affects API costs, latency, and context-window limits at scale. The piece also outlines the full generation pipeline, from user input through self-attention layers to the final response output. Rather than requiring deep mathematical knowledge, the guide aims to give product managers a practical mental model for making informed decisions about AI features.