AI Development with Large Language Models training course

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.