GKGaurav Kumaringauravbytes.dev·6d ago · 14 min readHow to Evaluate RAG Retrieval: A Practical Guide to Precision, Recall, MRR, MAP, and NDCGYou 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 AP53ALK
GKGaurav Kumaringauravbytes.dev·Jul 20 · 13 min readRAG Search Algorithms - How Retrieval Actually Works Under the HoodEvery agentic AI system that "looks things up" before answering — a support bot searching a knowledge base, a coding agent searching a codebase, a research assistant searching PDFs — is running a Retr31K
GKGaurav Kumaringauravbytes.dev·Jul 20 · 6 min readpgvector for RAG Search: Schema, Storage, and Retrieval, With Real ResultsRAG search has four moving parts: split the document, turn text into vectors, store those vectors somewhere queryable, and pull the right ones back out at query time. This post covers the last two — c00
GKGaurav Kumaringauravbytes.dev·Jul 7 · 8 min readChunking Strategies in RAG: A Deep DiveRAG systems live or die on one decision that happens before any embedding is computed: how the source document gets cut into pieces. This post walks through three chunking mechanisms available in Lang11K
GKGaurav Kumaringauravbytes.dev·Jul 7 · 10 min readUpgrading My PostgreSQL AI Agent: 3 Architecture Decisions I Made (And Why)This is Part 2 of an ongoing series on building a production-ready PostgreSQL AI agent using LangChain and Ollama. If you haven't read Part 1, start there — it covers the foundation: connecting to Pos00