LLM Token Generation Pipeline
Root Concept
LLMs generate text token by token using probabilities conditioned on context.
Concept Development By codeplu.com
Large Language Models (LLMs) starter concept workflow
What is Large Language Models (LLMs)?
LLMs generate text token by token using probabilities conditioned on context.
This starter module focuses on a simple but practical workflow so learners can build intuition before advanced topics.
How an LLM Responds
Tokenization
Text is split into tokens that the model can process numerically.
Inference
The transformer computes probabilities for the next token from context.
Decoding
A decoding strategy selects tokens until a complete response is formed.
Real World Example
Customer Support Assistant
A support assistant receives a question, processes context, and drafts a response token by token.
Prompt Input
User asks for refund policy details.
Context + Inference
The model uses policy docs and prior turns to rank likely next tokens.
Final Output
The assistant returns a structured and readable answer.
FAQs
Final Words
Mastering this Large Language Models (LLMs) workflow gives you a reliable base for advanced labs and projects.