Show HN: Design Patterns for AI (verificationdesign.com)

A new project titled "Design Patterns for AI" has been announced, focusing on creating agentic AI systems that prioritize evidence-based verification over subjective judgment. The initiative highlights the limitations of large language models (LLMs) in self-correction through naive self-review, addressing two primary failure categories: variance and bias. By implementing a series of design patterns, the approach aims to enhance the reliability of probabilistic systems, moving towards more deterministic behavior even when complete determinism is not feasible. Key patterns include the Causal Tag, which assigns stable identifiers to actions for accountability, and the Guardrail Decorator, establishing robust policy constraints at the model's output boundaries. This project is significant for the AI/ML community as it pushes for more transparent, accountable, and systematic processes in AI development, particularly for models that make decisions based on contextual data. The introduction of diverse verification techniques, such as the Delta and Adversarial Frame patterns, underscores a paradigm shift toward evidential reasoning and structured checks that minimize inherent biases and optimize performance. These frameworks not only improve AI reliability and control but also set the stage for more rigorous assessments of AI systems, paving the way for broader compliance and ethical standards in AI applications.