rlhf

Training a large language model is not a single act — it is a five-stage industrial process that begins with petabytes of scraped text, passes through weeks of GPU computation consuming millions of dollars of electricity, and ends with a model that can hold a coherent conversation. Each stage has a specific job, a distinct failure mode, and a measurable cost. Here is how the whole pipeline works, from raw data to deployed product.

How LLMs learn to be helpful: RLHF, RLAIF, DPO, and Constitutional AI compared. Methods used by ChatGPT, Claude, Llama, and open models.

What AI alignment means, the evidence on failures like reward hacking, the main techniques from RLHF to interpretability, and where researchers disagree.

RLHF (reinforcement learning from human feedback) explained simply — how ChatGPT and Claude learn from humans, the limitations, and what replaced it in 2026.

Not in five years, not after careful preparation — tomorrow. An AI that can do anything a human can, better. The world is not ready.