When Claude 3 Opus edged out GPT-4 on MMLU, GPQA, GSM8K, and most of the frontier evaluation benchmarks, the interesting question wasn't whether Anthropic had simply scaled harder — it was what training-time secret sauce actually got them there. This review breaks down the Claude 3 tech report covering Opus, Sonnet, and Haiku, and more importantly digs into the core algorithm behind Anthropic's alignment stack: Constitutional AI and the RLAIF training loop that powers it.

The walkthrough covers what makes Claude 3 tick across the three capability tiers, why the family is deliberately designed around an intelligence-speed-cost tradeoff rather than a single flagship model, and how RLAIF — reinforcement learning from AI feedback guided by a written constitution — sidesteps some of the scaling bottlenecks of pure RLHF that relies on human labelers. Expect to come away with a clearer picture of why the constitutional approach scales differently, how it drives improvements in analysis, forecasting, code generation, and non-English languages, and what it means for anyone trying to build a similarly aligned model. If you're tracking the frontier LLM race beyond the benchmark charts, this is a solid five minutes.