AI Recipe Generation

Welcome to this lesson on generating new recipes with AI! So far, you have learned how to interact with large language models (LLMs), structure prompts, and manage LLM calls in your application. Now, you will see how these skills come together to create a feature that generates unique cooking recipes based on a list of ingredients provided by the user.

Imagine you have a few ingredients in your fridge and want to know what you can cook. With AI-powered recipe generation, your app can suggest creative, step-by-step recipes instantly. This not only makes your app more helpful but also demonstrates the power of combining AI with backend development.

In this lesson, you will learn how to connect your backend logic to the AI, send ingredients lists, and return structured recipes to your users. By the end, you will understand the full flow of generating a recipe with AI and preparing it for use in your application.

Before we dive in, let’s briefly recall two important concepts from previous lessons:

• Prompt Templates: You learned how to use template files to create prompts for the AI. These templates include placeholders (like {{ingredients}}) that are filled in with real values before being sent to the AI.

• LLM Manager: The LLM Manager (typically a function like generate_response) is a helper that handles all communication with the language model. It loads the right prompts, fills in variables, sends the request, and returns the AI’s response.

These tools are the foundation for generating recipes with AI. In this lesson, you will see how they are used together in a real API view.

Let’s walk through how your app generates a recipe using AI, step by step.

Your app provides a specific function to handle the recipe generation request. Since this function receives data from the user, it is set up to handle POST requests.

from django.views.decorators.csrf import csrf_exempt
from django.views.decorators.http import require_http_methods
from .utils import _parse_json_body, _json_error

@csrf_exempt
@require_http_methods(["POST"])
def generate_recipe(request):
    payload = _parse_json_body(request)
    if payload is None:
        return _json_error("Invalid JSON body", 400)

    ingredients = payload.get("ingredients")
    if not ingredients:
        return _json_error("No ingredients provided", 400)

    ingredient_str = ", ".join(str(item) for item in ingredients)
    # ... (rest of the code)

• Decorators: @require_http_methods(["POST"]) ensures the endpoint only accepts POST requests, and @csrf_exempt allows the request to pass without a CSRF token (common for API endpoints).
• Body Parsing: We use a helper function _parse_json_body to manually extract the JSON data from the request.
• Validation: If the body is malformed or ingredients are missing, we return a structured error using _json_error.

Example request body:

{
  "ingredients": ["chicken", "rice", "broccoli"]
}