Course Structure and Goals

Having established the context of LLM workflows and the role of Python, let's outline the structure of this course and the specific skills you will acquire. Our primary objective is to equip you with the practical knowledge and tooling proficiency needed to build, test, and deploy functional applications using Large Language Models.

This course progresses logically from foundational setup to application deployment, as illustrated below:

This diagram shows the learning path, starting with fundamentals, moving through core tools and techniques, building specific applications like RAG, and finally covering testing and deployment practices.

Course Progression

1. Foundations (Chapters 1-3): We start by ensuring your development environment is correctly configured for LLM tasks. You'll learn the fundamentals of interacting directly with LLM APIs using Python, covering requests, responses, authentication, and error handling.
2. Core Skills & Frameworks (Chapters 4-6, 8): This block introduces the essential libraries that streamline LLM application development.
   • LangChain: You'll learn how to use LangChain to orchestrate complex workflows, manage prompts, parse outputs, and build chains and agents. We'll cover both fundamental and more advanced features.
   • LlamaIndex: We then focus on LlamaIndex for connecting LLMs to your private data sources. You'll learn about data loading, indexing strategies, and querying mechanisms.
   • Prompt Engineering: Woven into this section is a dedicated look at prompt engineering techniques implemented directly within Python, moving beyond basic prompting to structured and dynamic prompt generation.
3. Application Building (Chapter 7): With the core tools mastered, we'll focus on building Retrieval-Augmented Generation (RAG) systems. This involves integrating data indexing (LlamaIndex) and workflow management (LangChain) with vector stores to ground LLM responses in specific information.
4. Productionizing (Chapters 9-10): The final chapters address the practicalities of bringing LLM applications to life.
   • Testing & Evaluation: You'll learn strategies specifically designed for the challenges of testing LLM outputs, including unit/integration testing, evaluation metrics, and monitoring.
   • Deployment: We cover packaging your application (e.g., using Docker), creating API endpoints (e.g., with FastAPI), choosing deployment strategies, and adopting operational best practices like CI/CD.

Learning Goals

Upon completing this course, you will be able to:

• Set up a Python environment tailored for LLM development, including secure API key management.
• Interact programmatically with various LLM provider APIs.
• Use LangChain to design, build, and manage LLM workflows, including chains and agents.
• Employ LlamaIndex to load, index, and query external data sources for LLM applications.
• Construct and evaluate Retrieval-Augmented Generation (RAG) systems.
• Apply effective prompt engineering techniques within your Python code.
• Implement appropriate testing and evaluation methods for LLM-based systems.
• Understand and apply best practices for packaging, deploying, and monitoring Python LLM applications.

Throughout the course, hands-on practice sections will reinforce the material, allowing you to apply what you've learned immediately. This course is designed for learners with existing Python programming knowledge who want to specialize in building applications with Large Language Models. We assume you are comfortable with Python syntax, data structures, and standard library usage, but prior experience with LLMs or specific frameworks like LangChain or LlamaIndex is not required.