Generative AI Tutorial — Slide 79
A clear explanation of the concept shown in Slide 79, with examples, applications, and a technical breakdown.
A clear explanation of the concept shown in Slide 79, with examples, applications, and a technical breakdown.
Slide 79 illustrates how Generative AI uses embeddings to compare semantic meaning. Instead of matching text based on exact words, models convert words or sentences into high‑dimensional numeric vectors. These vectors allow the model to understand similarity and context, enabling tasks like search, recommendation, summarization, and reasoning.
Numerical representations of text that capture meaning, context, and relationships between concepts.
Measuring how related two pieces of text are by comparing distances between their embedding vectors.
A multi‑dimensional space where words and sentences with similar meaning lie close together.
User enters a phrase or sentence.
Model converts the text into a numeric vector.
Vector compared with others using cosine similarity.
Most relevant or similar items are returned.
Search engines using embeddings return results based on meaning instead of keywords.
Systems suggest similar articles, videos, or products using vector similarity.
Chatbots retrieve relevant knowledge base entries by comparing embeddings.
Grouping large sets of text using similarities in their vector space.
Embeddings capture semantic meaning, enabling better search, understanding, and reasoning.
No. Images, audio, and even video can be converted into embeddings.
No. Different models generate embeddings with different dimensions and characteristics.
Continue exploring how Generative AI transforms understanding and search.
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