Building a powerful Large Language Model (LLM) like ChatGPT or Claude isn’t a single-step process. It typically involves three crucial training phases that shape the model’s intelligence, usefulness, and alignment with human preferences. In this post, we’ll break down these stages in simple terms: Pretraining, Supervised Fine-Tuning (SFT), and Preference Tuning.
The journey begins with pretraining, where the model learns the structure and semantics of language itself. This stage uses enormous datasets—think books, websites, forums, and more.
During pretraining, the model is exposed to massive volumes of raw text and is tasked with predicting the next word (token) in a sentence. For example, if the model sees:
“The cat sat on the…”
…it learns to predict that “mat” might come next.
💡 This is called self-supervised learning—there are no labels; the structure of language is the supervision.
The result of this step is a base model (also called a foundation model). While it understands language really well, it’s not yet very helpful when you give it instructions like “Write a blog post about transformers.” Instead of doing the task, it might just list random information. That’s where fine-tuning comes in.
Once the base model is trained, we move on to Supervised Fine-Tuning (SFT). Here, the goal is to make the model better at understanding and responding to human instructions.
This is done by training the model on curated examples where both the input and expected output are provided. For example:
This kind of fine-tuning still uses next-token prediction, but now it’s done in a more controlled way, where the training pairs reflect real user prompts and responses.
Outcome: The model evolves from a general-purpose language generator to an instruction-following assistant.
SFT can also be applied to classification, translation, and other specific tasks, but it’s most commonly used to turn base models into helpful chatbots or agents.
The final step is Preference Tuning, also known as Reinforcement Learning from Human Feedback (RLHF) or Reward Modeling.
Here, we go beyond correctness—we teach the model to respond in ways that are safe, polite, and aligned with human expectations. This involves collecting multiple responses from the model, ranking them by human feedback (or proxies for it), and adjusting the model to favor the more desirable outputs.
It’s all about alignment—getting the model to respond not just correctly, but appropriately and ethically.
This step is crucial for deploying LLMs in real-world applications where tone, clarity, and trustworthiness matter as much as accuracy.
Let’s recap the three stages:
| Stage | Goal | Method | Outcome |
| Pretraining | Learn the structure of language | Self-supervised next-token prediction | Base model |
| Supervised Fine-Tuning | Learn to follow instructions | Labeled input-output pairs | Instruction-tuned model |
| Preference Tuning | Align output with human values | Human feedback or preference learning | Safe, aligned model |
Together, these stages transform an untrained architecture into a powerful, user-aligned LLM ready for practical use.