Building a Simple Data Analyst AI Agent with Llama and Flask

About this course

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 Who This Course Is ForBeginners 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

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