Vector embeddings and semantic search are a key part of the technology powering large language models (LLMs). They give AI the ability to understand the meaning and context of text and help you get more accurate, relevant answers.
In this episode of our Data and AI Engineering in Five Minutes series, Shivam Chandarana (Technical Lead, Softwire) will:
- Explain what vector embeddings and semantic search are
- Walk through the steps to create a vector embedding
- Highlight the critical role of vector indices
- Show vector embeddings power retrieval augmented generation (RAG)
