Semantic retrieval is a way of finding information based on meaning rather than matching exact words. You ask a question or describe what you need, and the system finds relevant results even if they use completely different wording. That gap between what someone types and what they actually mean is exactly what semantic retrieval is designed to close.
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What Is an Embedding?
One of the hardest things about building AI systems is that the things humans care about (words, sentences, images, ideas, etc) aren’t naturally something a computer can do math on. A computer doesn’t inherently know that “happy” and “joyful” are similar, or that a photo of a dog and the word “dog” are related. It just sees raw data.
Embeddings are the solution to that problem.
What is Retrieval-Augmented Generation (RAG)?
Large language models are impressive, but they have a fundamental limitation in that they only know what they were trained on. Ask a model about something that happened after its training cutoff, or about a document sitting in your company’s internal knowledge base, and it either makes something up or tells you it doesn’t know.
Retrieval-augmented generation, almost always shortened to RAG, is the approach the industry has settled on to fix this.
The idea is pretty straightforward. Instead of relying purely on what the model has memorized, you give it the ability to pull in relevant information from an external source, then use that information to generate a response.
What is an Embedding Model?
Computers are good at numbers. They’re not naturally good at understanding that “dog” and “puppy” are related, that a photo of a beach and the phrase “summer vacation” share something in common, or that a five-star review and the sentence “this product is amazing” mean roughly the same thing.
Embedding models are how we bridge that gap.