Content Validation: Guarding Against Truncated AI Output

In the devlog-ist/landing project, we're focused on delivering high-quality content. A crucial part of this is ensuring that AI-generated content meets our standards before it's published. The Problem: Silent Content Truncation AI models, particularly when generating longer pieces of content, can sometimes be cut short due to token limits or other constraints. This can result in incomplete or nonsensical posts being saved without any immediate indication of the issue. We needed a way to automatically detect and prevent this. The Solution: Post-Generation Validation To address this, we've implemented a post-generation content validation process. This validation occurs before the AI-generated content is persisted in the system. The validation checks for the following: Prism finishReason : We inspect the finishReason property returned by the AI model. If it indicates token-limit truncation, the content is flagged as incomplete. Minimum Word Count : We enforce a minimum word count to ensur