Dec 24, 2025 路 6 min read 路 HLD: Trade-offs at HLD level StepWhy this toolWhy NOT others PoolPartyBusiness semanticsLLMs can鈥檛 enforce ontology S3Cheap, durableDB too expensive SQSBackpressure + retryKafka costly + ops heavy EC2 preprocessingContinuous, low latencyBa...
Join discussionNov 4, 2025 路 7 min read 路 TL;DR:Retrieval-Augmented Generation (RAG) helps large language models (LLMs) like GPT-5 and Claude 4.5 access private, real-time, or domain-specific data by retrieving relevant chunks before generating answers. This article breaks down what RAG is, ...
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Sep 27, 2025 路 5 min read 路 In the rapidly evolving world of artificial intelligence, Retrieval-Augmented Generation (RAG) has emerged as a game-changing approach to building smarter, more accurate AI applications. If you've ever wondered how chatbots can answer questions about...
Join discussionSep 20, 2025 路 3 min read 路 Large Language Models (LLMs) have become the backbone of modern artificial intelligence, powering everything from chatbots to advanced research assistants. While these models are remarkably powerful, they sometimes produce responses that are inaccura...
Join discussionAug 22, 2025 路 6 min read 路 Retrieval-Augmented Generation (RAG) has become one of the most powerful techniques in the world of large language models (LLMs). At its core, RAG is simple: instead of relying only on the LLM鈥檚 memory, we fetch relevant knowledge from an external so...
Join discussionAug 22, 2025 路 9 min read 路 RAG looks simple on a slide. Index the data, retrieve a few chunks, let the model speak. Then reality shows up. Queries are messy. Sources are noisy. Latency bites. Costs creep. You ship a demo, but users catch the weak spots in five minutes. The goa...
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Aug 22, 2025 路 4 min read 路 So you鈥檝e built a Retrieval-Augmented Generation (RAG) system. Congrats! 馃帀 It fetches documents, stuffs them into your LLM, and spits out answers that (hopefully) make sense. But here鈥檚 the thing: vanilla RAG is like instant noodles鈥攆ast and easy, b...
Join discussionAug 22, 2025 路 5 min read 路 Retrieval-Augmented Generation (RAG) has become one of the most impactful paradigms for building intelligent applications, combining the generative power of Large Language Models (LLMs) with the grounding ability of external knowledge sources. Howeve...
Join discussionJun 5, 2025 路 10 min read 路 If you've ever dreamed of winning the Football World Cup and building a smart AI assistant in the same week, then you're in for a treat. Today, we鈥檙e diving into the RAG pipeline (Retrieval-Augmented Generation) but instead of your typical dry ML exp...
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Apr 19, 2025 路 2 min read 路 Let鈥檚 first understand what a basic RAG Retrieval-Augmented Generation looks like. Why do we need Parallel Query Retrieval? The above diagram explains how the flow of the User question leads to the answer.Although this flow works well, it has its fl...
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