An embedding is a numerical representation of the meaning of a piece of content, such as a word, sentence, document or image, expressed as a list of numbers known as a vector. Embeddings allow AI systems to mathematically compare how similar two pieces of content are in meaning, even if they use completely different words, which is the foundation for AI capabilities such as semantic search and retrieval-augmented generation.
How embeddings work
An embedding model converts a piece of content into a vector, a long list of numbers that represents its position in a high-dimensional conceptual space. Content with similar meaning ends up positioned close together in this space, even if the actual words used are quite different. This allows AI systems to find conceptually related content by measuring the mathematical distance between embeddings, which is exactly what a vector database is optimised to do quickly at scale.
Embeddings in practice
- A search tool uses embeddings to return relevant results for a query like "how do I get a refund" even when the matching help article is titled "returns policy", because the embeddings capture similar meaning despite different wording.
- A RAG system converts a library of company documents into embeddings, stored in a vector database, enabling fast retrieval of relevant content for AI-generated answers.
- A recommendation system uses product description embeddings to suggest similar items to customers based on conceptual similarity rather than shared keywords.
- A content moderation tool uses embeddings to detect text that is conceptually similar to known problematic content, even when specific wording has been altered to avoid simple keyword filters.
How Advantage approaches embedding-based AI solutions
Embeddings are a technical building block most relevant to custom AI development. Advantage advises on where embedding-based solutions, such as semantic search or RAG, genuinely add value beyond what standard Microsoft Copilot capabilities already provide.
Frequently Asked Questions
Are embeddings the same for every AI model?
No. Different AI models produce different embeddings, even for the same piece of text, because each model has learned its own way of representing meaning during training. Embeddings generated by one model are generally not directly compatible with a system built around a different model's embeddings.
Can embeddings represent things other than text?
Yes. While text embeddings are the most common in business applications, embeddings can also represent images, audio and other types of content, enabling similarity search and AI applications across these other media types as well.
Do I need to understand embeddings to use AI tools like Copilot?
No. Embeddings are a behind-the-scenes technical mechanism used within AI systems. Everyday users of tools like Microsoft Copilot do not need to understand embeddings to use these tools effectively, though understanding the concept helps when discussing more custom AI development projects.
