Gemini Embedding: Powering RAG and context engineering

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.