#vector-embeddings articles

NMNahid Mahmudnahid-mahmud555.hashnode.devAug 12 · 3 min read

Deep Dive into Vector Search: ANN, HNSW, and Production Optimization

Today, I took a deep dive into the core mechanics of modern AI systems, vector databases, and high-performance search architectures. Scaling search to millions of records requires smart engineering tr

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NMNahid Mahmudnahid-mahmud555.hashnode.devAug 10 · 3 min read

How Computers "Read" and Match Text: A Quick Vector Search Experiment

Ever wondered how modern AI systems, recommendation engines, and advanced search bars (like those on Notion, GitHub, or Google) instantly understand what you're looking for—even if you don't use the e

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GKGaurav Kumargauravbytes.devAug 3 · 14 min read

How to Evaluate RAG Retrieval: A Practical Guide to Precision, Recall, MRR, MAP, and NDCG

You implemented a search algorithm in your RAG system and it's returning data without any error, and you think that's it, you're done. You're wrong. That mindset works fine for a normal system — an AP

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ABAditya Biranjeadityabiranje210.hashnode.devJul 23 · 27 min read

Designing the Ingestion Layer of a Production RAG System

Most RAG postmortems point at retrieval. Wrong chunks come back, the reranker doesn't help, the LLM hallucinates around a gap in context. Almost none of it is a retrieval bug. By the time a query hits

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AGAvanish Gargavanish-garg.hashnode.devJul 22 · 6 min read

AI is an Illusion: Decoding the Greatest Magic Trick in Tech

The most beautiful high-dimensional calculus ever mistaken for a mind, which apparently now runs the world! WELCOME TO THE CURIOSITY CRUNCH — EPISODE 04: A series where we take the technical phenome

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APAnkita Patilblog.ankitapatil.devJul 16 · 5 min read

Building a Production-Style AI System: Designing VibeFit from Image Upload to Intelligent Recommendations

Live Demo → Experience the application. Architecture Walkthrough → Understand how the system works. Source Code → Explore the implementation. Project Deep Dive → Read the complete architecture and

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MMaverickjyanshu.hashnode.devJul 13 · 13 min read

Vector Embeddings

How AI Learned to Understand the Meaning Behind Words Who is this for? You don't need to know any math, code, or machine learning. If you've ever used Google Search, gotten a Netflix recommendation,

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SSSameer Selokarblogbysameer.hashnode.devJul 12 · 10 min read

Retrieval-Augmented Generation (RAG): What It Is, How It Works, and Why It Sometimes Fails?

Most people think connecting an AI model to your documents magically makes it accurate. The reality is more nuanced. Retrieval-Augmented Generation (RAG) significantly improves an LLM's ability to ans

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JCJim Chundevalelaccidentalcomplexity.hashnode.devJul 5 · 19 min read

Part 3: Open WebUI, a One-Sided Load Balancer, and a Bug Hiding One Field Deep

Two Birds, One Qdrant With the core database built and my own homelab knowledge ingested, I had a second idea. Open WebUI, the chat front end I already run for my Hailo and Gemma models, has its own b

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AAanchalaanchalfatwani.hashnode.devJun 30 · 7 min read

Beginner’s Guide to Embeddings: How AI Learns to Understand the World

Have you ever wondered how Netflix recommends a movie you actually want to watch? Or how an AI chatbot understands the emotional weight behind a sentence like "I'm drowning here" without checking the

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