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PSPriya Singhpriyasinghdev.hashnode.dev5d ago · 4 min read

First Time Implementing RAG

I built Repo Recall because I kept forgetting how my own projects worked. A few months after building something, I would look at my old code and have no idea what I had written. So I built a tool that

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MIMihir Inamdaraihive.hashnode.devSep 24 · 9 min read

I embedded all of Qdrant's docs with FastEmbed. The API would have cost 2 cents.

If you're building search on top of Qdrant, you hit this question early: do you embed your documents on your own machine, or pay OpenAI to do it? Running it locally sounds like the thrifty choice. No

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NPNiran Pravithanamarketdx.hashnode.devSep 19 · 11 min read

Cutting MCP Round-Trips with Jev (Part 3)

The ping-pong problem MCP pays every day MarketDX lets AI assistants (like Claude) pull market data through MCP — a set of "tools" the LLM can call itself: find stocks, pull financials, run screens, a

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SNSachin Nandanwarazureguru.netAug 26 · 12 min read

RAG for Microsoft AI Agent through TextSearchProvider and Redis

Earlier this year I had published an article on implemention of InMemory Vector Embeddings in Semantic kernel. At that time my focus was to understand how vector embeddings can be used in Semantic Ker

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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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Trending tags this week

First Time Implementing RAG

I embedded all of Qdrant's docs with FastEmbed. The API would have cost 2 cents.

Cutting MCP Round-Trips with Jev (Part 3)

RAG for Microsoft AI Agent through TextSearchProvider and Redis

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

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

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

Designing the Ingestion Layer of a Production RAG System

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

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