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PBPankaj Batrapankajbatra.hashnode.dev7h ago · 13 min read

I Deployed a Full RAG Backend for ₹0/Month — Here Are the 3 Walls I Hit

I just shipped the backend for YouTube RAG Chat: paste a YouTube link, chat with the video, and get AI-generated summaries, study notes, and quizzes — every answer grounded with clickable timestamp ci

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PKPuneet Kumarpuneetshivaay.hashnode.dev5h ago · 8 min read

Where RAG Fails: Understand the Limitations

Where RAG Fails: Understanding the Limitations of Retrieval-Augmented Generation Have you ever asked ChatGPT a question and received a confident answer that turned out to be wrong? This happens becaus

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SDShlok Dastrustingaisystems.hashnode.dev6h ago · 5 min read

I Stopped Trusting AI Answers. That’s When I Started Building Better AI Systems.

Generative AI is very good at sounding confident. That is both its biggest strength and one of its biggest problems. Ask an LLM a question and you can often get an impressive answer within seconds. Th

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BDBhuvesh Dhimanblog.bhuveshdhiman.com9h ago · 1 min read

RAG's top-k retrieval has a black box problem, and it isn't fixed by a bigger model

A vector search returns the nearest chunks by embedding distance, hands them to the model, and stops. Nobody asks why those chunks and not the ones one rank lower. When retrieval brings back the wrong

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CDCoding Dropletscodingdroplets.com1d ago · 14 min read

Hybrid Search in .NET: When to Use It and How for Better RAG Retrieval

The first RAG system I shipped answered beautifully about concepts and failed completely on part numbers. Ask it "how do I reset a stuck deployment" and it nailed the answer. Ask it "what does error P

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SRSukalyan Roysukalyanroy.hashnode.dev1d ago · 8 min read

Discovering RAGs 4: Project updates all the way

So far, we have covered a lot of the project. All the conceptual basics, going from the bare-bones structure of the project and right into features such as RRF. Where do we go from here? Well, there's

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DODUSHYANTH O Ahowragworksunderthehoodbanner.hashnode.dev1d ago · 7 min read

Demystifying Retrieval-Augmented Generation: An Engineering Deep-Dive into Document Processing, Vector Search, and Context Construction

1. Introduction & The Hallucination Problem Imagine deploying a state-of-the-art Large Language Model (LLM) to assist engineers with your company’s 500-page internal infrastructure specification or a

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MAMohammed Abdul Wahedwahedtechhashnodedev.hashnode.dev2d ago · 3 min read

Building a RAG AI Agent with Google ADK, Streamlit, and Cloud Run – My Hands-on Experience

Building a RAG AI Agent with Google ADK, Streamlit, and Cloud Run – My Hands-on Experience Introduction As part of the Google Cloud Gen AI Academy APAC Edition, I completed a hands-on codelab where I

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MKMudassir Khanmudassirworks.hashnode.dev1d ago · 7 min read

Agentic Search vs Traditional RAG: When One Query Is Not Enough

Agentic search is retrieval where an LLM plans the search instead of firing one embedding lookup. It decomposes a complex question into focused subqueries, runs them in parallel against your knowledge

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TKTuhin Kumar Duttatechtrail.tuhindutta.com2d ago · 9 min read

Building Scratchpad: Rethinking the Local AI Workspace

Most conversations around local AI still revolve around the models themselves. Which model should I run? How much VRAM do I need? Should I use a larger reasoning model or a smaller, faster one? Those

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Trending tags this week

I Deployed a Full RAG Backend for ₹0/Month — Here Are the 3 Walls I Hit

Where RAG Fails: Understand the Limitations

I Stopped Trusting AI Answers. That’s When I Started Building Better AI Systems.

RAG's top-k retrieval has a black box problem, and it isn't fixed by a bigger model

Hybrid Search in .NET: When to Use It and How for Better RAG Retrieval

Discovering RAGs 4: Project updates all the way

Demystifying Retrieval-Augmented Generation: An Engineering Deep-Dive into Document Processing, Vector Search, and Context Construction

Building a RAG AI Agent with Google ADK, Streamlit, and Cloud Run – My Hands-on Experience

Agentic Search vs Traditional RAG: When One Query Is Not Enough

Building Scratchpad: Rethinking the Local AI Workspace