Unlock the power of AI and build your own simple Data Analyst AI Agent without needing expensive APIs or heavy programming experience.

In this hands-on course, you will learn how to set up and run an open-source language model (Llama) locally and build a lightweight Flask app that can answer questions based on information stored in a Postgres database, similar to a simple Retrieval-Augmented Generation (RAG) system.

We start with the foundations of prompt engineering, introducing essential techniques like In-Context Learning (ICL), Chain of Thought (CoT), and Tree of Thought (ToT). You will practice creating, debugging, and refining prompts that guide your AI to better, more accurate answers.

Then, we move into building your first AI-powered app. You will set up a Flask server, connect it to a Postgres database, and build an endpoint that accepts user questions, processes them, and returns database answers through AI logic.

What You Will Learn

• How to install and run an open-source LLM model (Llama) on your own machine

• Core prompt engineering techniques and how they improve AI reasoning

• How to build a simple Flask application and connect it to a Postgres database

• How to process user input and deliver AI-generated answers from a database

How to install and run an open-source LLM model (Llama) on your own machine

Core prompt engineering techniques and how they improve AI reasoning

How to build a simple Flask application and connect it to a Postgres database

How to process user input and deliver AI-generated answers from a database

Who This Course Is For

• Beginners curious about AI, prompt engineering, and lightweight AI applications

• Data analysts who want to explore AI-enhanced workflows

• Developers interested in experimenting with Retrieval-Augmented Generation (RAG) principles

• Data engineers

• Anyone who wants a practical, fast, and clear introduction to using LLMs in real-world mini-projects

Beginners curious about AI, prompt engineering, and lightweight AI applications

Data analysts who want to explore AI-enhanced workflows

Developers interested in experimenting with Retrieval-Augmented Generation (RAG) principles

Data engineers

Anyone who wants a practical, fast, and clear introduction to using LLMs in real-world mini-projects

Requirements

• Basic knowledge of what a database is (no need to know SQL in depth)

• Python and SQL experience are helpful but not required; all key concepts are explained

• A willingness to learn and experiment

Basic knowledge of what a database is (no need to know SQL in depth)

Python and SQL experience are helpful but not required; all key concepts are explained

A willingness to learn and experiment

Whether you are taking your first steps into AI or looking for a practical project to enhance your portfolio, this course will help you build something real and functional while developing a strong foundation in prompt engineering and AI applications.

Enroll today and start building!