# Introduction to Post-training
Post-training transformed large language models from research artifacts into tools billions of people actually use. Before this technique emerged, LLMs remained largely inaccessible to the general public, confined to academic labs and a handful of well-funded companies.
Post-training refers to the process of fine-tuning and refining models after their initial training on raw internet data. This stage involves instruction tuning, where models learn to follow user commands, and reinforcement learning from human feedback (RLHF), which aligns model outputs with human preferences. These techniques dramatically improve model usability and safety compared to raw pretrained models.
The practical impact was immediate and obvious. GPT-3.5, OpenAI's post-trained version of GPT-3, could answer questions coherently and follow instructions reliably. Users could interact with it conversationally. The raw GPT-3 model before post-training often produced incoherent or unusable text when given natural language prompts.
This series from O'Reilly Radar examines how post-training became central to the AI industry. The technique proved so effective that it became standard practice across model developers. Anthropic built post-training into Claude from the start. Meta incorporated it into Llama. Every major frontier model now undergoes this refinement stage.
Post-training also introduced new research questions and challenges. Scaling these techniques to larger models requires novel approaches. The cost and data requirements of RLHF create bottlenecks. Questions around dataset quality, annotation consistency, and whether current methods actually produce alignment remain open.
The transition from inaccessible research tool to consumer product hinged on post-training. Without it, ChatGPT would never have achieved widespread adoption. The technique unlocked the billion-user market that defines AI's current era. Understanding post-training is