The original title is "AI Fundamentals - Part 1: From Prompt to Response"

Short summary

This guide explains how LLMs process input by converting text to tokens, managing them within context windows, and using transformer architecture with attention mechanisms. Using a travel planner example, it demonstrates that language models predict the next token sequentially—they don't retrieve answers. Understanding these mechanics helps developers design better AI applications and anticipate unexpected behavior.

• •Tokens, not words, are what LLMs actually process and it affects API pricing and limits
• •Context window limitations explain why models 'forget' earlier conversation details
• •Attention mechanisms allow models to weigh which tokens matter most when generating responses

Generated with AI, which can make mistakes.