Understanding Embeddings — Retrieval-Augmented Generation (RAG) & Vec…
Turning text into searchable vectors
Steps in Understanding Embeddings
- What Embeddings Actually Represent — beginner · Turning text into vectors that capture semantic meaning
- Choosing an Embedding Model — beginner · Trade-offs in dimensionality, cost and domain fit
- Similarity Metrics (Cosine, Dot Product, Euclidean) — beginner · How vector similarity is actually calculated
- Embedding Dimensionality and Trade-offs — beginner · Balancing retrieval quality against storage and compute cost
- Domain-Specific and Fine-Tuned Embeddings — beginner · When general-purpose embeddings aren't precise enough
Part of
- Retrieval-Augmented Generation (RAG) & Vector Databases roadmap — the full learning path