The opening installment of this three-part GPT-3 review sets up the paper that turned scaling from a research bet into industry gospel. Part one covers the introduction and approach: why fine-tuning on thousands of task-specific examples felt increasingly awkward, how humans learn new tasks from a handful of examples or a sentence of instructions, and why OpenAI decided that a 175-billion-parameter autoregressive Transformer trained on massive text corpora might close that gap.

The walkthrough dwells on the training setup, the dataset curation, and the crucial framing of few-shot, one-shot, and zero-shot evaluation as pure in-context prompting with no gradient updates. That framing is what made GPT-3 a platform rather than a model, and it is the same conceptual move now happening in speech with VALL-E-style in-context TTS and prompted ASR. Start here before parts two and three if you want the mental model before the benchmark tables. A solid grounding for anyone whose day job is building on top of LLMs or their audio-native cousins.