Tag: embeddings

This is a Google-side reference for the embedding layer behind retrieval and context engineering. It is worth keeping because the site is already leaning into Vertex AI, Gemini, and production-grade AI systems, where embeddings are infrastructure rather than a demo detail.

The useful question is not whether embeddings are good in isolation. It is whether the retrieval loop improves task success, preserves source traceability, and handles stale or missing context gracefully.

This visual guide is useful because embeddings become easier to reason about when they stop being abstract vectors and start feeling geometric. That matters for RAG, clustering, recommendations, search, and model evaluation.

I would keep it as a teaching aid, not as an implementation reference. The value is intuition: what similarity means, why representation quality matters, and why retrieval errors often start before the model answers.