Helpful Harmless Honest

Helpful, Harmless, and Honest, often abbreviated HHH, is the canonical three-part value framework introduced by Anthropic in a 2021 paper ("A General Language Assistant as a Laboratory for Alignment") for evaluating and training LLM assistants. The framework asserts that a good assistant should be helpful (effectively assisting the user with their task), harmless (avoiding outputs that could cause harm), and honest (not deceiving the user and acknowledging uncertainty). HHH became a foundational concept across the alignment community, influencing OpenAI's content policies, Anthropic's Claude training, the development of Constitutional AI, and many academic papers. The three properties often trade off — being maximally helpful on a borderline request may not be maximally harmless, being maximally honest about model limitations may be less helpful — and much alignment research focuses on optimizing the trade-off rather than maximizing any single dimension. AI governance teams use HHH as a structuring framework for AI compliance evaluation, often supplementing it with additional dimensions like fairness, transparency, and accountability that map to specific regulatory requirements like the EU AI Act.

HHH-aligned models with Centralpoint: Centralpoint routes to HHH-aligned models from OpenAI, Anthropic, Google, and other providers in a model-agnostic stack. Tokens are metered per skill and audience, prompts stay local, and policy-aware chatbots deploy through one line of JavaScript on any portal.


Related Keywords:
Helpful Harmless Honest,,