Many people are now considering how to bring generative AI and large language models to production services and facing several challenges. You may wonder, "How to integrate LLMs or AI chatbots with existing IT systems, databases, and business data?". "We have thousands of products. How can I let LLM memorize them all precisely?". Or "How to handle the hallucination issues in AI chatbots to build a reliable service?". A quick solution is "grounding with embeddings and vector search." What is grounding? What are embedding and vector search? This video discusses these crucial concepts to build reliable generative AI services for enterprise use. Resources: Original blog post → https://goo.gle/43sCOyh Stack Overflow semantic search demo → https://goo.gle/3OkeLgm Stack Overflow semantic search demo sample code → https://goo.gle/3Omj5fm Nomic AI visualization → https://goo.gle/3Y14Dwc Google Machine Learning Crash Course: Embeddings → https://goo.gle/3NRfOTy Vertex AI Model Garden → https://goo.gle/3pXIene Vertex AI Embeddings API for Text → https://goo.gle/44TufNN Vertex AI Matching Engine → https://goo.gle/44xeYCO Gen App Builder Enterprise Search → https://goo.gle/4798UlN Subscribe to Google Cloud Tech → https://goo.gle/GoogleCloudTech