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1. Embedding Fundamentals2. Hybrid Retrieval Strategies

Embedding Fundamentals

๐Ÿ“š Embeddings and Vector Searchโฑ 9 minโญ 75 XP

From Text to Vector Space

Embeddings map text into dense vectors where semantic similarity is measurable. This is the foundation of modern enterprise retrieval workflows.

  • Same intent, different wording should cluster closely.
  • Domain vocabulary quality depends on your chosen model and corpus.
  • Index maintenance and refresh cadence matter as much as model quality.
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What do embeddings enable in AI systems?
DNS routing
Semantic similarity search
IAM key rotation
Code compilation
Embedding Fundamentals Tutorial | Embeddings and Vector Search โ€” AWS Bedrock Academy