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Creating the base FastAPI Word Play Game Data

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Overview

This course walks learners through the foundation of the LLM prediction game: how the game works, how prompts are structured, how they are generated for each day, and how LLM responses are used and tokenized. By the end of this course, you’ll have a self-contained daily dataset powering the game logic.

Syllabus

Unit 1: Explaining the LLM Prediction Game Idea
Unit 2: Generating Prompt Data for the LLM Prediction Game

Expand Breakpoints and Nouns in Prompt Generator
Generate Questions for Each Noun Using List Comprehension
365-Day Prompt Generator
Save Entries to data.json

Unit 3: Selecting the Daily Prompt for the LLM Prediction Game

Implement load_all_prompts Function
Fix the Daily Prompt Selection
Validate Prompts Count in get_daily_prompt
Complete get_daily_prompt Function
Test Script for Prompt Selection System

Unit 4: Getting and Processing LLM Completions for the Game

Initialize the OpenAI Client
Implement get_llm_response_words Function
Add Error Handling to LLM Calls
Implement _split_into_words Function
Refactor get_llm_response_words to Output Word List
Standalone LLM Response Testing Script

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