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How vector embeddings help AI understand language context

Discover how vector embeddings and semantic search work, their role in LLMs and RAG, and steps to create them in our 5-minute Data and AI Engineering series.

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)

Author: Shivam Chandarana

Author: Helen Jackson

Principal Technical Consultant

13 August 2025

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About the AuthorHelen Jackson

Helen is a Principal Technical Consultant at Softwire, London, UK, and a qualified medical doctor with a PhD in auditory psychophysics. She draws on her broad background every day to lead tech teams solving business problems for her clients. She’s written code for money in the past, but these days prefers to focus on discovery work and business transformation. Helen loves people, technology, and complicated situations. She values curiosity, commitment, creativity, and kindness. She trains Softwire’s managers in communication skills and Softwire career processes, and is a passionate mentor to people in tech leadership roles at Softwire and outside.

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