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CodeSignal

Building Reusable LLM Components in Python

via CodeSignal

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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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