Python for LLM Workflows: Tooling and Best Practices
Chapter 1: Introduction to LLM Workflows with Python
What are LLM Workflows?
Common Components of an LLM Application
Why Python for LLM Development?
Overview of the Python LLM Ecosystem
Course Structure and Goals
Quiz for Chapter 1
Chapter 2: Setting Up Your Python Environment for LLM Development
Choosing Your Python Version
Virtual Environments (venv, conda)
Managing Dependencies with pip and requirements.txt
Essential Libraries Installation
Setting Up API Keys Securely
Development Environment Configuration Practice
Quiz for Chapter 2
Chapter 3: Interacting with LLM APIs using Python
Understanding LLM APIs
Making API Requests with the requests Library
Handling API Responses and Errors
Using Official Python Client Libraries
Authentication Methods
Rate Limiting and Cost Management Considerations
Practice: Querying an LLM API
Quiz for Chapter 3
Chapter 4: Core LLM Workflow Libraries: LangChain Fundamentals
Introduction to LangChain
Core Concepts: Models, Prompts, Output Parsers
Working with Different LLM Providers in LangChain
Creating and Using Prompt Templates
Parsing LLM Output Structures
Hands-on Practical: Building a Simple LangChain Application
Quiz for Chapter 4
Chapter 5: Advanced LangChain: Chains and Agents
Understanding Chains for Sequential Operations
Building Custom Chains
Introduction to Agents: LLMs as Reasoning Engines
Available Tools for Agents
Creating a Basic Agent
Debugging Chains and Agents
Practice: Implementing a Multi-Step Chain
Quiz for Chapter 5
Chapter 6: Data Handling for LLMs: LlamaIndex Basics
Introduction to LlamaIndex
Loading Data (Documents, Web Pages)
Indexing Data for Efficient Retrieval
Understanding Nodes and Indexes
Querying Your Indexed Data
Hands-on Practical: Indexing and Querying Documents
Quiz for Chapter 6
Chapter 7: Building Retrieval-Augmented Generation (RAG) Systems
Retrieval-Augmented Generation Concepts
Integrating LlamaIndex/LangChain for RAG
Vector Stores and Embeddings Overview
Setting Up a Basic Vector Store
Constructing a RAG Pipeline
Evaluating RAG Performance Metrics
Practice: Creating a Simple RAG Application
Quiz for Chapter 7
Chapter 8: Prompt Engineering Techniques in Python
Principles of Effective Prompting
Few-Shot Prompting Techniques
Structuring Prompts for Complex Tasks
Using Python for Dynamic Prompt Generation
Techniques for Reducing Hallucinations
Iterative Prompt Refinement
Practice: Developing and Testing Prompts
Quiz for Chapter 8
Chapter 9: Testing and Evaluating LLM Applications
Challenges in Testing LLM Systems
Unit Testing Components
Integration Testing Workflows
Evaluation Strategies: Metrics and Human Feedback
Using Frameworks for Evaluation
Logging and Monitoring LLM Interactions
Practice: Setting Up Basic Tests for an LLM Chain
Quiz for Chapter 9
Chapter 10: Deployment and Operational Best Practices
Packaging Your Python LLM Application
Containerization with Docker
Choosing a Deployment Strategy
API Endpoint Creation (FastAPI, Flask)
Monitoring Deployed Applications
Version Control and CI/CD for LLM Projects
Operational Considerations and Maintenance
Quiz for Chapter 10
Using Python for Dynamic Prompt Generation
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- The Python Tutorial, Python Software Foundation, 2024 - Provides fundamental guidance on Python's core features, including string formatting (f-strings), conditional statements, and looping constructs essential for dynamic prompt generation.
- Prompt templates (LangChain Documentation), LangChain, 2024 (LangChain) - Official documentation detailing the creation and use of
PromptTemplateobjects for structured and dynamic prompt construction within LangChain applications. - Prompt engineering (OpenAI Documentation), OpenAI, 2024 (OpenAI) - A guide from OpenAI that covers core principles for designing effective prompts, providing context, and refining outputs for large language models.
- LLM01: Prompt Injection (OWASP Top 10 for Large Language Model Applications), OWASP Foundation, 2023 (OWASP Foundation) - Addresses prompt injection, a significant security vulnerability in applications utilizing large language models, outlining its nature and mitigation strategies.
- Hands-On Large Language Models with Python: Build and deploy powerful NLP applications with the latest LLM techniques and frameworks, Ben Auffarth, 2024 (Packt Publishing) - A practical book demonstrating how to build and integrate LLM-powered applications using Python, including sections relevant to dynamic prompt construction and usage.
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