Claude's training involves two main stages: pre-training (learning language patterns) and alignment training (learning to be "helpful to humans"). The main alignment training methods are RLHF (guiding the model with human feedback preferences) and Constitutional AI (self-evaluation based on a set of explicit behavioral principles). Their combination enables Claude to generate useful responses while honestly acknowledging uncertainty and providing meaningful explanations when declining requests.
RLHF was systematized by OpenAI in the 2017-early 2020s and applied at scale in InstructGPT training, later becoming the core method for training ChatGPT. Anthropic's Constitutional AI is an innovation building on RLHF, addressing the problem of RLHF's dependence on human preference annotation — annotator biases and inconsistencies directly affect trained model behavior. Constitutional AI attempts to replace subjective preference judgments with explicit principles.
Understanding RLHF and Constitutional AI helps explain behavioral differences between Claude and other AI tools. Purely RLHF-trained systems are prone to "sycophancy" — tending to tell users what they want to hear rather than truthful answers. Constitutional AI's addition makes Claude notably different on this point: it's trained to remain honest even when users don't like the answer, which explains why Claude sometimes gives responses that differ from your expectations rather than simply echoing your viewpoint.
Translate understanding of RLHF and Constitutional AI into practical usage techniques: if you want honest feedback rather than flattery, explicitly tell Claude "I don't need you to agree with my viewpoint — I need you to tell me where the problems are"; if Claude declines your request, asking "why?" typically gets a meaningful explanation rather than a formulaic "I can't help with that"; if you're unsure whether Claude's answer is accurate, directly ask "how confident are you in this answer? Are there parts you're uncertain about?" — it's trained to honestly express uncertainty in these situations.