Generative AI Tutorial – Slide 68
Understanding the concept shown in Slide 68 with examples, applications, and a clear technical explanation.
Understanding the concept shown in Slide 68 with examples, applications, and a clear technical explanation.
Slide 68 introduces the concept of semantic embeddings in Generative AI. Embeddings convert text, images, or other data into dense numerical vectors that capture meaning. These vectors enable systems to compare concepts, search semantically, cluster ideas, and power Retrieval-Augmented Generation (RAG).
Embeddings represent meaning, allowing machines to understand relationships between words or ideas.
Data is encoded into high‑dimensional vectors where similar items cluster together.
By comparing vectors using cosine similarity, systems find the closest matching concepts.
Text, images, or objects are provided as input.
A transformer model converts the input into a vector.
Vectors are stored in a vector database optimized for similarity queries.
The system retrieves the closest vectors when given a new query.
A numerical vector representation of meaning.
They allow AI to compare ideas, retrieve relevant data, and reason more effectively.
No. Images, audio, and even multimodal content can be embedded.
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