14 articles tagged
Four Postgres-native vector search options, one decision: compare pgvector, pgvectorscale, ParadeDB and Lantern on recall, latency, hybrid search and cost, plus the scale ceiling where a dedicated engine wins.
What does it really cost to build an AI app in India in 2026? Build bands run ₹6–60 lakh, but the ongoing LLM, inference and vector-database bill is the line founders miss. A rupee-by-rupee breakdown for startup budgets.
Cohere Command A is a 111B open-weights model with a 256K context, 150% higher throughput than Command R+, and the same $2.50/$10 rate card. A 2026 migration guide, plus where Cohere fits against GPT-5.6 and Claude.
Shubham Saboo's Always-On Memory Agent stores memory in SQLite and consolidates it every 30 minutes with no embeddings. We compare it to RAG on cost, latency and scale, and when to drop the vector database.
RAG is the right first choice for most LLM applications, but not all. This guide shows when fine-tuning an open model beats RAG, what a LoRA run costs in 2026, and how eCorpIT delivers domain adaptation for Indian teams.
Agentic RAG makes multiple retrieval and reasoning passes, costing about 2.5x a single-pass classic RAG. A 2026 baseline study shows a lightweight query router recovering most accuracy at 28.1% token savings. When each
AWS made Amazon Bedrock Managed Knowledge Base generally available on 17 June 2026. It removes the vector store, the parsing pipeline and the retrieval orchestration. It also removes some control, and it is not in
AWS put Amazon Kendra into maintenance mode on 30 June 2026 and closes it to new customers on 30 July. Migrating to Bedrock Managed Knowledge Base drops you from 32 connectors to 7.
Gemini 3.5 Pro's 2M-token context reopened the long-context vs RAG debate. Here is the real cost math, the accuracy that breaks mid-window, and a decision framework for backend and ML engineers in 2026.
Amazon Bedrock AgentCore is AWS's platform for production AI agents. Its Web Search tool reached GA on 17 June 2026 at $7 per 1,000 queries. Build a cited, web-grounded agent over MCP, with code and pricing.
A senior-engineer comparison of RAG embedding models in 2026: NVIDIA Nemotron 3 Embed, OpenAI text-embedding-3, Cohere Embed v4 and open-weight Qwen3, with RTEB benchmarks, pricing and self-host break-even math.
Stanford found RAG-backed legal tools still hallucinate 17-34% of the time. Here is what an enterprise RAG knowledge assistant costs in 2026, which retrieval setup actually works, and how eCorpIT builds one.
Most pgvector-versus-vector-database comparisons quote one vendor benchmark from April 2025 that ran no filtered queries and tested an extension most teams do not install. Here is what the primary sources actually
From MIT's 95% pilot-failure finding to Gartner's agentic AI warnings, here are 7 AI delivery lessons for enterprise teams: scoping, grounding, evaluation, cost control, governance, and DPDP-ready privacy.
