What is Embeddings?
A way of representing words, sentences, or other data as lists of numbers that capture their meaning.
Why It Matters
Embeddings allow AI to understand that similar concepts are related, enabling semantic search and recommendations.
Real-World Example
In an embedding space, 'dog' and 'puppy' would have similar number representations because they mean similar things.
“Understanding terms like Embeddings matters because it helps you have better conversations with developers and make smarter decisions about your software. You do not need to be technical. You just need to know enough to ask the right questions.”
Related Terms
Vector Database
A database designed to store and quickly search through embeddings, which represent data as lists of numbers.
Semantic Search
Search that understands the meaning behind your query rather than just matching exact words.
Tokenisation
The process of breaking text into smaller pieces called tokens that an AI model can process.
Large Language Model (LLM)
An AI system trained on massive amounts of text that can understand and generate human language.
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Related Terms
Large Language Model (LLM)
An AI system trained on massive amounts of text that can understand and generate human language.
Tokenisation
The process of breaking text into smaller pieces called tokens that an AI model can process.
Vector Database
A database designed to store and quickly search through embeddings, which represent data as lists of numbers.
Semantic Search
Search that understands the meaning behind your query rather than just matching exact words.
Transformer
A type of AI architecture that processes text by paying attention to relationships between all words at once, rather...
Attention Mechanism
A technique that lets AI models focus on the most relevant parts of the input when generating output.