AI Engineering

AI Engineering is a collection of practical AI projects, experiments, tutorials, and applied research notes focused on building and understanding real-world AI systems. This section covers applied machine learning, deep learning, foundation models, AI agents, and hands-on experimentation with modern AI tools.

The goal is to make AI engineering easier to understand through concrete examples, beginner-friendly explanations, and practical implementation notes. These articles are written for AI learners, engineers, enterprise architects, researchers, and technology leaders who want to understand how AI systems are designed, tested, and applied in real-world scenarios.

Agentic AI – LLM with SQL – Beginner Bootcamp

AI Agent_LLM with SQL

In this post, you’ll learn how to build your own AI SQL Agent — a tool that transforms natural language into SQL queries and executes them on a real database.

In real-world teams, everyone — from marketers to HR to analysts — wants to “talk to the database” without needing to know SQL. But data lives behind structured queries. That’s where LLMs like GPT-4 shine: they understand natural language and, with the right schema and safety controls, can generate SQL queries that pull live data.

No prior coding experience required. Just download the code, run it, enhance it — and start learning!

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Agentic AI – LLM with RAG – Beginner Bootcamp

AI Agent_LLM with RAG

In this post, you’ll learn how to build your own document-reading AI assistant — an intelligent tool that can read and understand documents like PDFs, Word files, Excel sheets, HTML pages, JSON files, and more using OpenAI’s powerful language models combined with a cutting-edge technique called RAG (Retrieval-Augmented Generation).

No prior coding experience required. Just download the code, run it, enhance it, and start learning!

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Agentic AI – LLM with Web Scraping – Beginner Bootcamp

In this beginner-friendly bootcamp, learn how to create a smart web agent that scrapes websites, processes content with GPT-4, and answers user questions intelligently. You’ll start with basic LLM-based querying and then upgrade to a scalable Retrieval-Augmented Generation (RAG) system using vector databases like FAISS. Perfect for Python and AI learners!

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Agentic AI – LLM with API Calls – Beginner Bootcamp

🧠 Build Your First Agentic AI!
Curious how ChatGPT can actually call functions and fetch live data?

In our latest Beginner Bootcamp, learn how to create a smart Weather Agent using OpenAI’s GPT-4 and WeatherAPI — no advanced coding required!

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Agentic AI for Beginners – Learn by Building Your First App

🚀 Build Your First Agentic AI App (With Code Example!)

In this beginner-friendly bootcamp, you’ll learn how to build a multi-agent AI application from scratch. We’ll walk you through the core architecture of agentic systems and show you how to bring it to life with a real-world project — an AI-powered Workshop Planner and Meeting Assistant.

You’ll get:

✅ Step-by-step tutorial

💡 Ready-to-run Python code

🔧 Hands-on experience with LangChain and LangGraph

Just download the notebook, run it in your Python environment (like Google Colab), and start building. Tweak it, expand it, and you’re on your way to creating your own AI agents!

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How to Install and Run Your First Local LLM on Your Laptop

Have you ever used ChatGPT or other GenAI tools online? Imagine running one of these smart language models directly on your laptop—no coding experience required. In this guide, I’ll show you how to install a local LLM (Large Language Model) like DeepSeek and LLama2, using Ollama and run it within a Jupyter Notebook. All you need is a laptop with an internet connection. Simply follow the steps and copy-paste the code. Let’s get started!

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