5d ago · 12 min read · TLDR: RAG (Retrieval-Augmented Generation) gives an LLM access to your private documents at query time. You chunk and embed documents into a vector store (ChromaDB or FAISS), retrieve the relevant chunks at query time, and inject them into the LLM's ...
Join discussionFeb 15 · 10 min read · Author’s Note I’ve been gone for long. I’ve been serving my nation (National Service) and have been using the time to relax, learn different skills and upgrading myself in different ways. Unexpectedly, I also went full-on into my other hobby… Playing...
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Dec 22, 2025 · 11 min read · Imagine you're on a vast electronics e-commerce site like samsung.com. You're surrounded by hundreds of products across dozens of categories. You have specific needs- "a durable camera for hiking" or "a quiet laptop for a student" - but the website's...
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Nov 20, 2025 · 5 min read · Building a robust RAG (Retrieval-Augmented Generation) system isn’t only about retrieving information, it’s about creating conversations that feel contextual, continuous, and intelligent. One of the biggest challenges in achieving this is managing ch...
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Oct 17, 2025 · 4 min read · “This is a reflection based on my recent experience looking for a new job, combined with a chatbot I built using my own candidate profile. The chatbot is designed to simulate first-round interviews and could help filter real job opportunities more ef...
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Sep 9, 2025 · 4 min read · The rise of Large Language Models (LLMs) has transformed how we build applications—whether it’s AI-powered chatbots, semantic search engines, recommendation systems, or Retrieval-Augmented Generation (RAG) pipelines. But here’s the challenge: LLMs n...
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Sep 6, 2025 · 5 min read · Introduction to RAG and Why It Matters Retrieval-Augmented Generation (RAG) has revolutionised how we interact with large language models by combining the power of information retrieval with generative AI. In this comprehensive guide, we'll build a c...
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