Demystifying AI Large Models: Architecture, Principles, and Workflow

AI Large Models

Large Language Models (LLM) are deep neural networks trained on massive data and billions of parameters. Their essence is a super‑large probability model that predicts the most likely next token given a context, without true understanding of the world.

Model Architecture

The strength of LLMs rests on three pillars:

Transformer architecture (structural foundation) – The Transformer discards recurrent structures and relies on Self‑Attention to process the entire sequence in parallel. Before Transformers, RNN/LSTM models could only remember recent tokens, losing earlier information.

Self‑Attention mechanism – This core component lets the model scan the whole text at once and assign higher importance to relevant words. For example, in the sentence “那个银行不给开户，因为它没钱”, Attention instantly links the pronoun “它” to “银行” rather than “开户”.

Pre‑training – The most resource‑intensive phase, where clusters of thousands of H100 GPUs run for months to ingest trillions of tokens from sources such as Common Crawl, GitHub, and research papers. The model learns grammar, factual knowledge, and basic programming logic.

Fine‑tuning & alignment (SFT & RLHF) – Instruction fine‑tuning teaches the model conversational formats, while Reinforcement Learning from Human Feedback (RLHF) lets humans score multiple model responses, guiding the model to produce safer, more useful, and human‑like outputs.

Model Principles

LLMs operate by continuously predicting the next token. This token‑level prediction drives all capabilities such as dialogue, writing, reasoning, and code generation. Token cost directly impacts API pricing, inference latency, GPU consumption, and context length.

Example of the prediction process:

<ol>
<li>今天天气真</li>
</ol>

The model predicts the next most likely token:

<ol>
<li>好</li>
</ol>

Continuing the generation:

<ol>
<li>啊</li>
</ol>

Final output:

<ol>
<li>今天天气真好啊</li>
</ol>

Inference flow:

Input Prompt → Tokenization

Tokenized input → Transformer computation

Transformer output → Next‑token prediction

Loop to generate subsequent tokens