Insights & Essays.
Field notes for CTOs and principal engineers: what shipped, what failed, and how to staff the next AI program. Depth first — no vendor recap.
Latest Articles
Team Topologies for AI-Era Engineering: Stream-Aligned Teams and Cognitive Load
How Team Topologies manages developer cognitive load in 2026. Guide to stream-aligned, enabling, and platform teams for AI engineering.
RAG 2.0: Hybrid Search, Graph Memory, and Agent Context Layers
RAG 2.0 evolves beyond simple vector retrieval. Explore hybrid search pipelines, graph-based agent memory, and dynamic context window management.
Scaling pgvector and Qdrant: Hierarchical Partitioning and Hybrid Metadata Search
Architectural blueprint for scaling vector search to billions of embeddings.
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What is “Scaling pgvector and Qdrant” about?
Architectural blueprint for scaling vector search to billions of embeddings.
Who wrote this article and when was it published?
Written by Vatsal Shah, an AI Leader and Solution Architect based in Ahmedabad, India, and published on June 15, 2026. It is filed under AI Engineering.
How can I apply this in my own organization?
Use the sections of this article as a working checklist, adapt each step to your platform and risk profile, and record the decisions you make. To discuss Scaling pgvector and Qdrant for your team, request a briefing at https://shahvatsal.com/contact.