Embeddings Turn Text Into Numbers. But There’s More Than One Way to Do It
Hello, I'm Rijul, and I'm building LiveReview — a blast-radius aware AI code review built for your business-critical systems. Star us to help devs discover the project, give it a try, and share your feedback to help improve the product. If you have read about RAG, you will probably be familiar with the concept of embeddings . We take a bunch of text and turn it into numbers. That's it. But there…
Embeddings are a method of converting text into numerical representations for AI applications such as code review and retrieval-augmented generation (RAG). There are two primary approaches to embeddings: sparse representations and dense embeddings. Sparse representations are akin to a large dictionary, where each word in the sentence is assigned a value based on its relevance.
This results in most values being zero, creating a sparse vector. Dense embeddings, on the other hand, represent text as a small vector with many numerical values, emphasizing the semantic meaning of the text. While sparse representations excel at capturing exact word meanings, dense embeddings prove more effective in handling synonyms, paraphrasing, and conversational queries by focusing on the overall meaning rather than individual words.
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