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“From Print to Prompt: 50+ Pythonic Concepts Every LLM Agent Engineer MUST Know (If You Want to Build Real AI Apps)” Artificial Intelligence has changed the world in unbelievable ways. Just five years ago, writing programs that could reason , analyze a PDF , solve logic problems , or run automated workflows sounded like something only Elon Musk , OpenAI , or Big Tech engineers could do. But today? With Python and modern open-source Large Language Models (LLMs), any developer can build their own AI agent : That executes tasks Reads files Searches the web Calls APIs Makes decisions And even talks like a human But here’s the hidden truth: You cannot become a powerful AI/LLM engineer if your Python fundamentals are weak. People jump directly into LangChain , CrewAI , AutoGen and skip understanding the Pythonic foundations behind LLM agents: Modular pipeline coding Context management Dependency control Data validation Async execution Functio...

“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...