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I am building an AI based platform that can read and selsct resumes based on the matching algorithms such as cosine similarity to give matching scores for candidate resumes against job desc. The platform can provide users with sourcing analytics and

🚨 Everyone says "RAG is dead" — but I say: It’s just been badly implemented. I’ve worked on AI systems where Retrieval-Augmented Generation (RAG) either changed the game… or completely flopped. Here’s the hard truth 👇 --- 🤯 Most teams mess up

🚀 Excited to share my latest YouTube video! demonstrate how to integrate Microsoft's Semantic Kernel with Qdrant, the open-source vector database, to build an intelligent question-answering system for Wikipedia pages citeturn0search3 In this tu

Day 14 of learning AI/ML as a beginner. Topic: Word2vec I think I am getting lost and that I have omitted some core concepts as there are many things I believe I am unfamiliar with and I am searching for some guidance. Can anybody please tell me wh

If you're obsessed with problem-solving and have the ability to think outside the box, keep reading! ⏬ We at LeXploR are hunting for razor-sharp software interns with an obsession for building from scratch and a passion for democratizing legal knowl

That hack worked partially, but this prompt is a gem 💎. Yesterday, I posted how you can whitelist your lovable built website using lovable. I posted that just for meme content, but later I decided to try it, and it worked a little bit. I got this i

Now I will briefly explain how we buid my https://medial.app/idea/turtal-ai-caaaf91d0ac7f Our main unique feature is - 1)Turtal AI lightweight customizable turta IDE😊 2) coder rankboard 💻 3) AI job searching 🗜️ 1)Turtal AI lightweight customizab

Fine-tune vs Prompt vs Context Engineering. Simple step-by-step breakdown for each approach. 𝗙𝗶𝗻𝗲-𝗧𝘂𝗻𝗶𝗻𝗴 (𝗠𝗼𝗱𝗲𝗹-𝗟𝗲𝘃𝗲𝗹 𝗖𝘂𝘀𝘁𝗼𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻) 𝗙𝗹𝗼𝘄: 1. Collect Data → Gather domain-specific info (e.g., legal docs). 2. Sta

3 ways how most AI systems are built. I’ve explained each one step-by-step. 1) 𝗧𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗔𝗜 (𝘀𝘁𝗲𝗽-𝗯𝘆-𝘀𝘁𝗲𝗽) 1. 𝗦𝗲𝘁 𝘁𝗮𝘀𝗸 – Decide what problem the model should solve. 2. 𝗖𝗼𝗹𝗹𝗲𝗰𝘁 𝗱𝗮𝘁𝗮 – Gather lots of example