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CodeSignal

AI Recipe Generation & Extraction with Django

via CodeSignal

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Overview

Add an LLM layer to generate and extract structured recipes. You’ll create a lightweight prompt templating system, integrate OpenAI calls behind a small wrapper, and expose an endpoint that generates recipes from ingredients. You’ll also build a CLI workflow to extract a clean recipe from messy HTML and store it into your Django database.

Syllabus

  • Unit 1: Introduction to LLM Calls
    • Initialize OpenAI Client
    • From Pirate to Tutor: Using System Prompts to Shift AI Persona
    • Modify User Prompt to Ask About List Comprehensions in Python
    • Temperature Tuning for Diverse LLM Outputs
    • OpenAI Model Selection Experiment
  • Unit 2: Prompt Structure and Variables
    • Implement Template Loading Function
    • Replace Template Placeholders with Input Value
    • Custom Placeholder Replacement in render_template
    • Implement render_prompt_from_file Function
    • AI Recipe Generator Prompt Template
  • Unit 3: Building LLM Manager
    • Complete LLM Manager Setup with OpenAI Client and Logging
    • Implement generate_response Prompt Rendering Logic
    • Integrate OpenAI Chat Completions into LLM Manager
    • Add Robust Error Handling to LLM Manager
  • Unit 4: AI Recipe Generation
    • Create Prompt Templates for Recipe Generation
    • Implement Input Validation for generate_recipe View
    • Integrate AI Recipe Generation
    • Recipe Response Parsing Foundation
    • Implement Recipe Parsing Logic
    • Finalize Recipe Generation Endpoint with Structured JSON Output
  • Unit 5: Extracting Recipes From HTML
    • Create Recipe Extraction Prompt Templates
    • Complete extract_recipe_from_html Function
    • Implement parse_recipe_string Function for Structured Recipe Extraction
    • Store Extracted Recipe Data in Database
    • Recipe Extraction Command-line Interface

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