Скачать или смотреть Use Continuous Query and LLM to Understand Customer Feedback

🚀 Use #ContinuousQueries and #LLM to scale customer feedback analysis?

In my latest video, I’m exploring a simple yet powerful approach to automate sentiment extraction from customer feedback at scale. By combining Continuous Queries with large language models (LLM), this setup lets you interpret each piece of feedback with just a prompt and a quick API call – no complex infrastructure required!

Whether you're already familiar with Continuous Queries or just hearing about them now, this method could significantly streamline your data workflows. Some of the services are still in preview, but there’s no better time to test out their potential and see how robust these tools really are.

Here’s what you’ll find in the video:

1. 📐 System Design - 01:11

2. 🧪 Synthetic Data for Customer Feedback - 02:29

3. 🔄 The Continuous Query Publishing Data to Pub/Sub - 03:21

4. ☁️ Cloud Run Service to Extract Sentiment - 03:51

5. 📊 Viewing Results in Logs & Canvas - 05:18

6. ⚙️ Concurrency Improvements in Continuous Queries - 07:09

7. ❓ Why Not ML.GENERATE_TEXT()? - 08:13

8. 💬 Thoughts on Cost: Present and Future - 10:41

Ready to dive in? Check out the video for all the details!

#SystemDesign #LLM #PubSub #GenAI #ContinuousQuery #BigQuery #Gemini #CloudRun #BI #looker

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