AI Development with Large Language Models: From Prompts to Production

Remember your first conversation with ChatGPT? That moment when you realized AI could understand and respond to natural language? Now imagine building sophisticated AI applications that can do far more than just chat. This hands-on course will show you how to harness the full potential of Large Language Models (LLMs) to create powerful, practical applications.

What sets this course apart is its focus on practical implementation. Rather than just discussing theoretical concepts, you’ll spend most of your time writing code and building working solutions. Each module culminates in a practical exercise that demonstrates key concepts and provides you with working code that you can adapt for your own needs.

Starting with prompt engineering fundamentals, you’ll learn how to craft effective prompts that consistently generate high-quality responses. You’ll then move beyond the OpenAI playground to build Python applications that interact directly with the API, implementing proper error handling and response processing. The course covers advanced techniques like Retrieval-Augmented Generation (RAG), showing you how to enhance LLM responses with your own data sources.

You’ll also gain hands-on experience with the Hugging Face ecosystem, learning how to leverage pre-trained models for specific tasks like sentiment analysis and text classification. By the end of the day, you’ll have practical experience with both the OpenAI API and Hugging Face tools, along with working code examples that you can build upon for your own projects.

Whether you’re looking to add AI capabilities to existing applications or build new AI-powered solutions from scratch, this course will give you the practical skills and understanding you need to work effectively with Large Language Models.

Course Outline

Module 1: Foundations of Prompt Engineering

Theory and Concepts:

• Understanding the anatomy of effective prompts and their impact on LLM responses
• Implementing system messages, user messages, and assistant messages effectively
• Exploring temperature, top-p sampling, and their effects on response generation
• Best practices for prompt design and common pitfalls to avoid

Practical Exercise: Build a customer support prompt template that consistently generates high-quality responses across different scenarios. Test and refine the prompt using the OpenAI playground.

Module 2: Working with the OpenAI API

Theory and Concepts:

• Setting up and configuring the OpenAI Python client
• Understanding API authentication, rate limits, and best practices
• Implementing basic API calls using Python
• Handling API responses and error conditions gracefully

Practical Exercise: Create a Python script that interfaces with the OpenAI API to build a simple question-answering system. Implement proper error handling and response processing.

Module 3: Advanced Prompt Engineering Techniques

Theory and Concepts:

• Implementing chain-of-thought prompting for complex reasoning tasks
• Creating structured output using format specifications
• Designing prompts for specific use cases (classification, extraction, generation)
• Building prompt templates for consistent results

Practical Exercise: Develop a system that takes unstructured text input and extracts structured data in JSON format using carefully crafted prompts. Implement chain-of-thought reasoning to handle complex cases.

Module 4: Retrieval-Augmented Generation (RAG)

Theory and Concepts:

• Understanding the principles and benefits of RAG architectures
• Implementing vector databases for efficient information retrieval
• Creating embeddings using OpenAI’s embedding API
• Building a complete RAG pipeline with Python

Practical Exercise: Build a question-answering system that uses RAG to provide accurate answers based on a provided document collection. Implement document chunking, embedding generation, and similarity search.

Module 5: Introduction to Hugging Face

Theory and Concepts:

• Overview of the Hugging Face ecosystem (Transformers, Datasets, Tokenizers, and Hub)
• Understanding key NLP tasks and available models
• Working with the Transformers library and pipelines
• Best practices for model selection and usage

Practical Exercise: Create a sentiment analysis application using Hugging Face’s Transformers library. Implement text classification using pre-trained models and compare results across different model architectures.

Throughout the course, each module builds upon the previous ones, with practical exercises designed to reinforce learning through hands-on implementation. You’ll leave with working code examples that you can adapt and extend for your own projects.

The focus is on practical, hands-on learning, with theory introduced as needed to support the implementation work. By the end of the day, you’ll have built several working AI applications and gained practical experience with both the OpenAI API and Hugging Face tools.

All exercises are designed to work in both local Python environments and browser-based environments like Google Colab, ensuring flexibility regardless of local installation constraints.

Intended Audience

This course is designed for software developers, data scientists, and technical professionals who want to build practical applications with Large Language Models. It's ideal for teams looking to integrate AI capabilities into their software products, or individuals wanting to understand how to effectively leverage LLMs in production environments.

Prerequisites

Those attending this course should meet the following:

• Working knowledge of Python programming
• Basic understanding of REST APIs
• Familiarity with JSON data structures
• Basic command-line experience
• Understanding of software development principles