The REAL potential of generative AI

TL;DR

The video explains how to build differentiated applications on large language models (LLMs) by customizing them through fine-tuning, reinforcement learning from human feedback (RLHF), and capturing production feedback data. The key to standout apps—illustrated by ChatGPT’s five-day million-user milestone—was fine-tuning the same base model.

Key insights

• LLMs are statistical models of words trained to predict the next word; scaling parameters and data forces them to acquire world knowledge and reasoning.
• ChatGPT’s huge success came from fine-tuning the same base model (DaVinci 3) using instruction-tuning and RLHF, not from a new architecture.
• RLHF dramatically improves performance: a 1–2 billion parameter model with RLHF was preferred over the full GPT-3 (100× larger) in the InstructGPT paper.