If you'd like to check out the source code to this solution, visit our Github repositories below: Content Lab UI: https://github.com/mongodb-industry-solutions/content-lab-ui Content Lab Search Microservice: https://github.com/mongodb-industry-solutions/content-lab-backend-search Content Lab Writing Assistant Microservice: https://github.com/mongodb-industry-solutions/content-lab-backend-writing-assistant Subscribe to MongoDB YouTube→ https://mdb.link/subscribe This video covers how to streamline editorial operations with GenAI and MongoDB to gain real-time insights, accelerate drafting, and ensure reliable content delivery. Our interns showcase the demo they built and explain how they developed this solution with a microservices architecture. Read the related blog: https://mdb.link/5sdjP7Pl48A-blog Solutions Library: https://mdb.link/5sdjP7Pl48A-solutions Chapters: 00:00 - The Modern Content Challenge 00:32 - Why Traditional Workflows Fail 01:39 - The High Cost of Delay 02:20 - Solution: Introducing Content Lab 02:54 - Live Demo: Choosing a Persona 03:15 - Analyzing Trending Data 04:00 - Discovering & Filtering Topics 04:40 - The AI Writing Assistant 05:45 - Applying a Persona with the Refine Tool 06:29 - Saving & Publishing Your Draft 07:09 - The Technical Magic Behind Content Lab 07:30 - Our Microservices Architecture 08:22 - Leveraging MongoDB Atlas & Vector Search 09:02 - How MongoDB Processes & Enriches Data 10:04 - Our Flexible Data Model in MongoDB 11:11 - Using Aggregation Pipelines & Atlas Vector Search #mongodb #media #demo #db #nosql #genai Visit Mongodb.com → https://mdb.link/MongoDB Read the MongoDB Blog → https://mdb.link/Blog Read the Developer Blog → https://mdb.link/developerblog

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