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
Contextual Embeddings That Retrieve the Evidence, Not Just the Answer: Perplexity's Late-Chunking Architecture, pplx-embed-v2-context-9b-preview, and Multi-Evidence Synthesis
An in-depth architectural breakdown of Perplexity's pplx-embed-v2-context-9b-preview, late chunking across 32k tokens, 1024-d int8 vectors, and empirical evidence recall on context-bench.
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.
External Standards & Publications
Our engineering publications follow industry standards for software architecture and agentic AI systems. Read more about technical specifications at the IEEE Standards Association and general web engineering principles at the World Wide Web Consortium (W3C).
What is “RAG 2.0” about?
RAG 2.0 evolves beyond simple vector retrieval. Explore hybrid search pipelines, graph-based agent memory, and dynamic context window management.
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 19, 2026. Reading time is 20 min read. It is filed under AI / Architecture.
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 RAG 2.0 for your team, request a briefing at https://shahvatsal.com/contact.