This n8n workflow automates the process of retrieving the latest YouTube video data, generating structured metadata using a language model, and updating the video details on YouTube. It begins by monitoring a specified YouTube channel for new videos through an RSS feed trigger. When a new video is posted, the workflow uses Apify to scrape video information and wait until the dataset is ready. It checks if metadata has already been generated; if not, it employs an AI language model to analyze the video subtitles and create a short preview, timestamps, and relevant tags. The generated data is then formatted and used to update the YouTube video’s description and tags seamlessly. This workflow is particularly useful for content creators and marketers aiming to automate metadata management, improve video SEO, and streamline content updates—saving time and ensuring consistency across videos.

Node Count	>20 Nodes
Nodes Used	@n8n/n8n-nodes-langchain.chainLlm, @n8n/n8n-nodes-langchain.lmChatMistralCloud, @n8n/n8n-nodes-langchain.outputParserStructured, code, httpRequest, if, noOp, rssFeedReadTrigger, stickyNote, wait, youTube