May 2 · 4 min read · If you build AI applications, you need to understand embeddings. They power semantic search, RAG systems, recommendation engines, clustering, anomaly detection, and dozens of other applications. Yet most developers treat them as a black box. Let's op...
Join discussionApr 17 · 12 min read · RAG now drives 51% of enterprise AI implementations — up from 31% just a year ago. Behind every one of those pipelines is a vector database making millisecond-level similarity decisions across millions or billions of embeddings. Choosing the wrong on...
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Apr 7 · 17 min read · A client came to me in early 2024 with a fully built AI agent that kept timing out in production. The retrieval step took 800ms on average and occasionally spiked past two seconds. Their users were abandoning queries. The "AI is too slow" complaint w...
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Mar 9 · 8 min read · The $340 invoice showed up on a Tuesday in late January. Not wild money in isolation, but this was for an internal RAG tool serving exactly 11 people — my team at a fintech startup — and when I did the math, that was $30 per user per month for what a...
Join discussionFeb 13 · 9 min read · Content Role: pillar RAG System Setup Guide with Pinecone and OpenAI Embeddings Retrieval Augmented Generation (RAG) has become the de facto architecture for building AI systems that need to answer questions accurately using proprietary or domain-sp...
Join discussionFeb 12 · 4 min read · Pinecone Vector Database: Store and Search AI Embeddings The Mistake That Cost Me $2000 in API Bills Three weeks ago, I learned this lesson the hard way. Let me save you from the same pain. Table of Contents Why This Matters Now Understanding the Fu...
Join discussionFeb 9 · 3 min read · How Adeloop Uses RAG for Unstructured Data — and Why Structured Data Needs a Different Approach When people hear RAG (Retrieval-Augmented Generation), they often assume it is a universal solution for all data problems.In practice, this is not true. ...
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Feb 5 · 4 min read · Quick Answer: To check if Pinecone is down, visit apistatuscheck.com/api/pinecone for real-time monitoring, or check the official status.pinecone.io page. Common signs include index creation failures, upsert timeouts, query latency spikes, rate limit...
Join discussionNov 4, 2025 · 14 min read · In the world of AI assistants, getting a correct answer is harder than just getting a fast one. Large Language Models (LLMs) are notorious for "hallucinating" or confidently providing answers based on outdated information. For developers, this is a c...
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