Building Reusable LLM Components in Python

Overview

Learn to design a prompt-driven workflow for LLM apps. Build a Prompt Manager for templates with defaults and a robust LLM Manager that wraps OpenAI API calls.

Through hands-on examples, you'll manage prompts cleanly, inject dynamic context, handle errors, and structure interactions for real-world use.

Syllabus

• Unit 1: Design of Our Deep Researcher
• Unit 2: Making Basic LLM Calls
Setting Up Your OpenAI Client
Changing Personas with System Prompts
Crafting Effective User Prompts
Controlling Randomness with Temperature Settings
Selecting the Right LLM Model
• Unit 3: Prompt Structure and Variables
Loading Templates from Files
Replacing Placeholders with Regular Expressions
Integrating the Prompt Generation Pipeline
Creating a Recipe Generator with Templates
• Unit 4: Creating the Prompt Manager
Implementing Template Variable Substitution
Adding Template Logging Functionality
Complex Templates for Dynamic Prompts
Executing the prompt
• Unit 5: Creating the LLM Manager
Adding Prompt Logging for Debugging
Enhancing API Error Handling
Optimizing Boolean Response Detection
Validating Environment Variables for Security
Creating a Flexible LLM Wrapper Function

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Subjects

Computer Science