Building a large language model from scratch is usually best understood as a sequence of stages rather than a single training run.

At a high level, the workflow looks like this:

1. Convert raw text into model inputs.
First, we need a way to represent text numerically. This usually means splitting text into tokens, mapping these tokens to integer IDs, and turning those IDs into embedding vectors. At this stage, we also decide how long the model’s context window should be and how to create input-target training pairs for next-token prediction.

2. Implement the attention mechanism.
The core idea behind modern LLMs is self-attention. Self-attention allows each token to weigh the relevance of earlier tokens in the sequence when computing its representation. In GPT-style models, this attention is causal, meaning the model can only attend to the current token and tokens to its left, not future tokens.

3. Assemble a transformer-based language model.
Once the tokenization and attention pieces are in place, we stack them into a full model. A GPT-like LLM typically combines token embeddings, positional information, multi-head self-attention, feed-forward sublayers, residual connections, normalization layers, and a final output head that predicts the next token.

4. Pretrain the model on unlabeled text.
The model is then trained on large text corpora with a self-supervised objective, usually next-token prediction. During this stage, the model is not taught explicit task labels such as “spam” or “not spam.” Instead, it learns useful statistical structure in language, such as syntax, semantics, facts, writing style, and many recurring patterns in text.

5. Finetune the pretrained model for a specific goal.
After pretraining, we can adapt the model to downstream tasks. Two common examples are finetuning for text classification and finetuning to follow instructions in a chat-like setting. The important idea is that pretraining gives the model broad language competence, while finetuning makes it more useful for a concrete application.

6. Use the model for generation and evaluation.
Finally, we use the trained model autoregressively: it generates one token at a time, appends that token to the context, and repeats. In practice, this stage is often paired with evaluation, inference optimizations such as KV caching, and optionally a user interface so the model can be interacted with more conveniently.

In short, a modern LLM workflow usually moves from text preprocessing to attention and model construction, then to self-supervised pretraining, and finally to task-specific finetuning and inference. This staged view is useful because it makes clear that “building an LLM” is really a pipeline of separate but closely connected components.