In response to challenges faced with validation in large language model (LLM) streaming, the streaming architecture was revamped to improve efficiency and maintain context without incurring excessive compute costs or latency. Previously, validation was performed on the entire accumulated output, which maintained context but was costly and redundant. The new architecture allows validators to specify the amount of context needed, whether a sentence, paragraph, or the entire output, before producing a validation result. This improves responsiveness and reduces computational demands while ensuring that context-dependent validations, like those checking for politeness or personal information, are accurate. The updated streaming architecture, now live in Guardrails, supports all validators and offers instructions for enabling streaming, enhancing the overall responsiveness and robustness of applications using LLMs.