What are embeddings used for?#
By converting concepts into numbers, embeddings allow AI to grasp these relationships. This enables sophisticated functions like searching for information based on meaning (not just keywords), recommending highly relevant products, or grouping customers into meaningful segments for marketing campaigns.
How do embeddings work?#
An AI model analyses a massive dataset, learning the context and relationships between different items. For example, by reading text from the internet, it learns that the word "invoice" often appears near words like "payment," "due date," and "vendor," but rarely near "vacation" or "recipe." Based on these learned relationships, the model assigns a specific list of numbers (a "vector") to each item.
Items with similar meanings or contexts are assigned numerically similar vectors. This places them close to each other in a conceptual map that the computer can navigate. The AI can then measure the mathematical "distance" between items to find the closest matches, enabling it to find similar documents, recommend related products, or identify patterns that a human might miss.