An LLM calls a tool by generating tokens the application reads as a request. Here is how a model uses tokens to decide when to reach for a calculator or code.

Tools let LLM agents do more than talk: search, run code, call APIs, and change files. Here is what tools are and how they let AI systems take real-world actions.

Inference is how an LLM actually generates a reply, one token at a time. Here is how the token-by-token process works under the hood, from prompt to finished output.

GPT is a model and ChatGPT is a product built around it. Here is the difference between an LLM and an AI application, and why the distinction actually matters.

LLM reasoning means the model works through intermediate steps before answering. Here is why thinking step by step produces better outputs, and when it does not.

LLMs are stateless and forget everything between calls, yet ChatGPT remembers your chat. The trick is resending the conversation each turn. Here is how it works.

Tokens are the chunks of text LLMs actually read, usually pieces of words. Here is what tokens are, why models use them instead of words, and why they matter.

A large language model predicts one token at a time to generate text. Here is what an LLM actually is, how it is trained, and how it produces a response.

Sharing your AI coding learning journey builds both expertise and visibility. Here is how writing, shipping, and teaching in public compound into a real reputation.

Becoming an expert agentic coder works best as a structured, multi-phase curriculum. Here is how to phase your learning and track real progress along the way.