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Knowledge Base AI Architectures RAG / Retrieval

RAG / Retrieval

8 articles
How-to 28 Jul 2026

Context Engineering for Agentic RAG: Caching and Cost Control

An agentic RAG system retrieves repeatedly and carries results forward, so context grows across a trajectory and dominates cost. Prefix caching, compaction and retrieval budgets are what keep a deployment…

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Sizing guide 28 Jul 2026

Multimodal RAG: GPU Planning for Document, Image and Video Retrieval

Most enterprise knowledge is in scanned documents, diagrams, screenshots and recordings, not clean prose. Multimodal RAG indexes those directly, and the GPU cost sits overwhelmingly in ingestion rather than in…

7 min readRead →
Comparison 28 Jul 2026

Long Context vs RAG in 2026: Where Retrieval Still Wins

Million-token windows did not make retrieval obsolete. Published comparisons put long-context serving at orders of magnitude more cost per query than a RAG pipeline, and multi-fact recall degrades in the…

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15 Jul 2026

Adaptive RAG in 2026: Routing Between Hybrid, Graph and Agentic Tiers

Enterprise RAG in 2026 is a routed portfolio: hybrid retrieval absorbs most lookups, GraphRAG handles cross-document reasoning at heavy index-time cost, and agentic loops multiply tokens 5-20x per hard question.…

5 min readRead →
12 Jul 2026

RAG GPU Server Reference Architecture for India

A RAG GPU server for India needs fast vector retrieval, low-latency LLM inference, and local data residency in a single coherent architecture. The right design balances HBM-class GPU memory for…

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8 Jul 2026

RAG Storage and Retrieval Sizing for Enterprise Search

Sizing RAG storage and retrieval for enterprise search requires careful consideration of GPU capabilities, data governance, and workload benchmarks. The NVIDIA H200's 141 GB HBM3e memory enhances data-center acceleration, while…

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8 Jul 2026

Reference Architecture for RAG on H200 GPU Servers

A RAG stack is a retrieval, storage, inference, and governance system, not just a vector database attached to a model. The practical design uses H200-class GPU servers for generation, CPU/storage…

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6 Jul 2026

Right-Sizing a GPU Server for Enterprise RAG

In a Retrieval-Augmented Generation (RAG) stack, the generator LLM dominates GPU sizing — a 70B model needs ~1 GPU (H200) at FP8/4-bit — while the embedding model and vector database…

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