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MSMohammad-Hadi Shadmehrshadmehr.euJun 13 · 7 min read

What does the retrieval ecosystem look like in the age of agents?

This week I attended "Retrieval in the Age of Agents", a meetup organized by Qdrant and hosted at the Merantix AI Campus in Berlin. The event brought together representatives from Qdrant, Haystack (de

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MVMohit Vermaaiwithmohit.hashnode.devApr 27 · 3 min read

Naive RAG Is Dead: The 4-Layer Architecture That Boosts Accuracy by 40%

Naive RAG Is Dead: The 4-Layer Architecture That Boosts Accuracy by 40% The Problem: Naive RAG Faithfulness is Only 0.54 Naive RAG systems have a critical flaw: faithfulness of just 0.54. This means approximately half of your answers misrepresent the...

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OMO Melinggothartech.hashnode.devApr 26 · 8 min read

The Librarian Principle: Why Your RAG System Doesn't Need a Vector Database

Originally published at Gothar Tech Part of our 2025 software architecture series. The Librarian Principle The standard RAG diagram has become liturgy. Embed. Chunk. Store in a vector database. Retrieve by cosine similarity. Stuff into context. Gen...

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RSRahul Sehrawatai-zero-to-hero.hashnode.devApr 13 · 11 min read

RAG Failure Modes: Seven Ways Your Retrieval Will Lie

You built the RAG system. You chunked well, embedded with a frontier model, added hybrid search, wired pgvector, and stood up evals. Your numbers look good. You ship. A week later, a user reports a wrong answer. You dig in and find the system retriev...

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RSRahul Sehrawatai-zero-to-hero.hashnode.devApr 12 · 11 min read

RAG — Giving the Model a Library Card

Imagine two students sitting the same exam. The first student walks in empty-handed. They have to answer every question from whatever is already in their head. If they studied, they'll do fine on the topics they studied. If they hit a question on som...

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AGAditya Guptaadiyogiarts.hashnode.devMar 28 · 6 min read

Agentic RAG: Building Thinking Retrieval Systems

Originally published at adiyogiarts.com Have you ever asked an AI a complex question, only to receive an answer that’s confidently, eloquently, and completely wrong? This phenomenon, the “polite hallucination,” is the critical flaw of many modern AI...

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DADaniel Anthony Romitelli Jrcraftedbydaniel.hashnode.devMar 10 · 6 min read

My RAG Stack for Code Retrieval: pgvector HNSW + Metadata Filters + Reranking (and the Parts I Refuse to Guess About)

I sat down to write the pgvector section of this post—the HNSW index DDL, the reranker batching, the metadata filter shapes—and realized I kept reaching for the wrong file. The query I was proud of wasn’t the vector search. It was dedupeRagChunks. Th...

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MRMoon Robertsynsun.hashnode.devMar 8 · 8 min read

RAG in the Wild: What I Learned After Two Weeks of Chunking Experiments

Three months ago I shipped a RAG pipeline that I was genuinely proud of. Semantic search over our internal docs, OpenAI embeddings, Pinecone on the backend. It felt modern. Then someone on our team asked it "what's our parental leave policy?" and it ...

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MAMd. Amirul Islamainahid.hashnode.devFeb 2 · 36 min read

DDIA: Chapter-3

যারা সবকিছু পরিপাটি করে রাখে, তারা মূলত খোঁজাখুঁজির কষ্ট করতে চায় না বলেই এমনটা করে। - German Proverb মৌলিকভাবে একটি ডাটাবেসকে দুটি কাজ নিখুঁতভাবে করতে হয়: ১. ডেটা স্টোর করা: যখন আপনি ডাটাবেসকে কোনো তথ্য দেবেন, তখন সেটি যেন সুরক্ষিতভাবে জমা রাখে। ২....

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SHSiva Hemanth Kollasivahemanthkolla.hashnode.devJan 29 · 5 min read

Retrieval-Augmented Generation for Enterprise Knowledge Systems

1. Introduction Retrieval-Augmented Generation (RAG) has emerged as a powerful approach for deploying large language models (LLMs) in enterprise knowledge systems. Traditional LLMs rely solely on knowledge encoded during training, which makes them pr...

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Trending tags this week

What does the retrieval ecosystem look like in the age of agents?

Naive RAG Is Dead: The 4-Layer Architecture That Boosts Accuracy by 40%

The Librarian Principle: Why Your RAG System Doesn't Need a Vector Database

RAG Failure Modes: Seven Ways Your Retrieval Will Lie

RAG — Giving the Model a Library Card

Agentic RAG: Building Thinking Retrieval Systems

My RAG Stack for Code Retrieval: pgvector HNSW + Metadata Filters + Reranking (and the Parts I Refuse to Guess About)

RAG in the Wild: What I Learned After Two Weeks of Chunking Experiments

DDIA: Chapter-3

Retrieval-Augmented Generation for Enterprise Knowledge Systems