Essential for 2026 Agentic RAG. Skills involve implementing 'Hybrid Search' (combining BM25 keywords with semantic vectors), handling expansive multimodal embed
Vector database skill covers the retrieval tier of AI systems: choosing and operating stores (pgvector to dedicated engines), designing indexes and metadata filters, tuning hybrid keyword+vector search, and keeping embeddings synchronized with source truth at scale.
Strong and structural: every RAG and agent-memory system needs this tier operated well, and engineers who can tune retrieval infrastructure measurably outperform generic backend profiles in AI teams.
pgvector: it teaches the concepts inside familiar Postgres and covers a surprising share of production needs. Add one dedicated engine afterward to learn scale features.
Highly: indexing math, hybrid search, and sync-pipeline design are universal; product APIs are thin layers over the same concepts.