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  My LLM Journey: From Curiosity to Building Real AI Agents 🚀 My LLM Journey: From a Curiosity to Building Real AI Agents 💡 The Big Question: What Skills Are Future-Proof? I'm currently in my first year of BSc Computer Science at FICS, and like many others, I found myself constantly grappling with a central question: What skill won’t be replaced in the future? What will still be in high demand? I spent days searching online, often feeling frustrated. With the AI boom, I saw lots of people fearing that their jobs would disappear. But my focus wasn't on fear; I was trying to find the area that would gain real traction in the future. After much reflection, I realized the world is fundamentally changing. Companies no longer need thousands of workers for routine tasks. This led to a more specific question: What can I create that can’t be easily replaced, that allows me to be my own owner, that people truly value, and that represe...

“How I’m Making My AI Agent Smarter Than Prompt Chains — From Debugging to Deployment” 👋 Intro: From Prompt Follower to Real Thinker Let me be honest — I’m still learning. I’m a 2nd-year BSc CS student trying to build production-level LLM tools. But this past month, I realized something dangerous: Most AI agents are just smart parrots. They don’t remember, adapt, or think. And if you don’t design better logic, they never will. That’s when I made a decision: I won’t build just another chatbot . I’ll build autonomous AI agents that actually get smarter with time. Here’s how I’m upgrading my agents — even as I learn and debug daily. 🔧 Problem: Agents Are Dumb (By Default) At first, I built my agent using simple tools: LangChain chains Prompt templates Basic task routing But every time I gave it a complex task — like evaluating a resume or suggesting a career path — the agent would: Forget what I said earlier Repeat generic answers Hallucinate fac...

"How I Built a Smart Resume Reader U sing RAG and LLM – A Step-by-Step Guide for Beginners " Ever thought of making an AI that reads resumes like a recruiter? Well, I did exactly that — and in this blog, I'll show you how I built a Resume Reader using RAG (Retrieval-Augmented Generation) and LLM (Large Language Model) from scratch. Whether you're an aspiring AI developer or someone building cool tools to automate HR tasks — this is a perfect project to level up your Python + AI skills. 🧠 What Is a Resume Reader LLM? A Resume Reader LLM is a smart application that: ✅ Reads a resume (PDF/TXT) ✅ Understands the content using LLM ✅ Answers questions like “What is the candidate’s experience?”, “What are their top skills?”, etc. ✅ Uses RAG to improve accuracy by combining retrieval (searching your data) + generation (via LLM) 🧰 Tools & Tech Used Python 3.10+ LangChain – to connect documents + LLM OpenAI / HuggingFace LLM FAISS – f...