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Case study: ChatGPT

Definition​

ChatGPT is a family of conversational LLMs from OpenAI. They are trained with supervised fine-tuning and reinforcement learning from human feedback (RLHF) to follow instructions and converse safely.

They illustrate the full LLM stack: pretrained base model, instruction tuning, and RL-based alignment (RLHF). The same ideas (instruction tuning, preference optimization) appear in open and other proprietary models. Use case: chat, prompt-driven tasks, and agent-like workflows with tools.

How it works​

Start from a base model (e.g. GPT-4): a decoder-only transformer pretrained on next-token prediction. Instruction tuning: fine-tune on (instruction, response) pairs so the model follows user intent. RLHF: train a reward model on human preference data (which of two responses is better); then optimize the policy (the LLM) with reinforcement learning (e.g. PPO) to maximize the reward. The result is a model that is helpful, follows instructions, and is less likely to produce harmful or off-policy content. Safety and guardrails (content filters, refusals, monitoring) are applied in the product. Prompt engineering and RAG or agents extend the system for specific use cases.

Use cases​

ChatGPT-style systems fit chat, writing, code help, and task automation that benefit from instruction-following and tool use.

  • Conversational assistants and customer support
  • Writing, summarization, and brainstorming
  • Code help, tutoring, and task automation via chat

External documentation​

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