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“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...