Building AI Tools with LLMs and Gradio

Getting Started with LLM Engineering

I've been diving into the world of Large Language Model (LLM) engineering through an excellent Udemy course led by industry veteran Ed Donner. This comprehensive program takes you through real-world projects that demonstrate practical applications of AI technology in business scenarios.

Currently about 25% through the course, I've already had hands-on experience with multiple AI platforms including OpenAI, Anthropic, and locally running models like Ollama. The course covers advanced techniques such as RAG (Retrieval-Augmented Generation), QLoRA (Quantized Low-Rank Adaptation), and AI Agents - all essential tools in the modern AI developer's toolkit.

The FlightAI Assistant Project

One of the most engaging projects I completed was the AI-powered customer support assistant for a fictional airline called FlightAI. This project perfectly demonstrates how multiple AI services can work together to create a comprehensive user experience.

The assistant leverages several cutting-edge AI models:

GPT-4o-mini for natural language chat interactions
DALL-E-3 for generating images of destination cities
OpenAI's TTS-1 for converting text responses to speech

You can check out the complete project code in my GitHub repository.

Discovering Gradio for Rapid Prototyping

One of the most valuable tools introduced in the course is Gradio, an open-source Python library that's a game-changer for AI developers. Gradio enables you to quickly build user-friendly web interfaces for AI models, APIs, or any Python function with minimal code.

What makes Gradio particularly appealing is its ability to:

Create professional-looking demos in minutes
Generate live URLs for instant sharing
Provide an intuitive interface without requiring web development expertise

The speed at which you can go from a working AI model to a shareable web application is truly impressive.

Development Environment and Setup

The course utilizes a robust development stack:

Anaconda platform for managing Python environments and dependencies
Jupyter Lab as the primary development environment for working with notebooks, code, and data

While the course materials use Jupyter notebooks, I decided to export my project to a standalone Python file. This approach gave me more flexibility to run the application outside of the Jupyter platform and better understand the underlying code structure.

Learning About Function Calling with LLMs

One of the most fascinating concepts I encountered was the ability to extend LLM capabilities through function calling. This technique allows the AI model to execute custom functions as part of its response generation process.

In the airline assistant project, I implemented this by:

Creating custom functions (like get_ticket_price)
Passing these functions as tools to the chat completions API
Allowing the LLM to intelligently decide when to call these functions

For example, when a user asks about ticket prices, the LLM automatically triggers the get_ticket_price function, which searches through a dictionary of city names and prices to return accurate pricing information. This seamless integration of custom logic with AI responses creates a much more powerful and practical application.

Conclusion

The combination of powerful AI models, intuitive development tools like Gradio, and techniques like function calling opens up endless possibilities for creating practical AI applications.

The experience of building a multi-modal AI assistant that can chat, generate images, and speak has given me a solid foundation for tackling more complex projects as I continue through the course. I'm excited to explore the remaining 75% of the curriculum and see what other innovative applications I can build.