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Building a Simple Data Analyst AI Agent with Llama and Flask - AD-TEAM - 05-26-2025
![[Image: 0e2a0e299edc915ba85ba941b4670ceb.jpg]](https://i125.fastpic.org/big/2025/0526/eb/0e2a0e299edc915ba85ba941b4670ceb.jpg)
Building a Simple Data Analyst AI Agent with Llama and Flask
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 2h 47m | 1.32 GB
Instructor: Kiril Spiridonov
An Introduction to Prompt Engineering and AI-Powered Apps
What you'll learn
- Understand and apply core prompt engineering techniques such as In-Context Learning (ICL), Chain of Thought (CoT), and Tree of Thought (ToT).
- Set up and run an open-source large language model (Llama) locally without needing paid APIs.
- Build a simple AI-powered Flask application that connects to a Postgres SQL database.
- Design prompts that enable an AI agent to understand user questions and retrieve accurate answers from structured data.
- Develop a basic understanding of connecting natural language processing with SQL databases through APIs.
Requirements
- Basic understanding of what a database is.
- Python and SQL experience are helpful but not required - all key concepts are explained clearly during the course.
- Access to a computer where you can install Python packages and run Docker containers.
Description
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
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!
Who this course is for:
- Beginner to intermediate learners curious about prompt engineering, LLMs, and AI agents.
- Data analysts and Python developers who want to enhance their skills with AI tools.
- Tech enthusiasts who want to build a real-world project combining AI, databases, and web APIs.
- Anyone interested in building their first simple Retrieval-Augmented Generation (RAG)-style application.
- Data engineers
Homepage
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