New Course: Build SQL AI Agent from Scratch
Hi friends! 👋
I’m super excited to share the release of my new LinkedIn Learning course — SQL Agents with Large Language Models!
This course introduces you to the world of AI agents by guiding you step-by-step through building an SQL AI agent from scratch with Python.
The term AI agent might sound fancy, but at its core, it’s simply a set of functions that take a user’s question, process it with an LLM, and return an answer. The goal of an SQL AI agent is to answer user questions with data — by translating the question into a SQL query, sending it to a database, and returning the relevant results.
Here’s what we’ll cover in the course:
We’ll start by exploring the architecture of an SQL AI agent, using the following design:
Next, we’ll dive into each component to understand its role. Since LLMs play a critical part in this process, we’ll learn how to work with the OpenAI Python SDK to send prompts to different LLM APIs and interact with chat completion models:
In my view, the prompt template is the most critical component of any AI agent. We’ll explore how to design and optimize prompts for generating SQL and how to automate prompt creation with templates.
Finally, we’ll connect all the agent components into a complete, working system that can answer user questions with data.
This course is beginner-friendly and assumes no prior experience with AI agents.
Happy learning!
Rami
This piece really got me thinking, especially your point that the prompt template is the most critical component. It makes me wonder how much of that prompt template optimization is transferable to agents working with other types of data?