Welcome to the first lesson of our Building an Image Generation Service with PHP course! In this course, you'll learn how to build a complete web application that transforms text descriptions into stunning images using the Official Gemini API for image generation.

Before we dive into PHP frameworks or API integration, we need to establish a solid foundation for our image generation system. At the heart of any AI image generation service is the prompt — the text instructions that guide the AI in creating the image you want.

Prompt engineering is the art and science of crafting effective instructions for AI models. When working with image generation models, the quality and structure of your prompts directly impact the quality of the images you receive. A well-crafted prompt provides clear direction, specific details, and appropriate context to help the AI understand exactly what you're looking for.

In our application, we'll be creating event banners for a fictional company called Eventify Co. Rather than crafting a new prompt each time a user requests an image, we'll create a template system that:

1. Maintains consistent structure and quality across all prompts.
2. Allows users to customize only the specific event details.
3. Handles the formatting and presentation of the prompt automatically.

This approach ensures that our application produces high-quality, consistent results while still allowing for customization.

Later in the course, we'll connect this prompt system to the Gemini API's gemini-3.1-flash-image-preview model to generate the actual images. Let's begin by understanding what makes an effective prompt template.

A well-structured prompt template for image generation typically contains several key components that work together to guide the AI. Let's examine the structure of our template:

Let's break down each section:

ROLE: This establishes the persona the AI should adopt. By positioning the AI as a lead graphic designer, we're setting expectations for high-quality, professional output.

THEME: This is where we'll insert the user's input — the specific event details they want to feature in the banner. Notice the {user_input} placeholder, which we'll programmatically replace with actual content.

TASK: This section clearly defines what we want the AI to create — an event banner with specific characteristics. It provides direction on how text should be integrated into the design.

Now that we understand the structure of our prompt template, let's create the actual file that will store it. In our application, we'll organize files according to their function, with templates stored in a dedicated directory.

First, let's set up our project directory structure:

The data/ directory will store our template and potentially other data files. The models/ directory will contain our PHP classes, and main.php will be our entry point for testing.

Now, let's create the image_prompt_template.txt file in the data/ directory with the content we discussed in the previous section. You can use any text editor to create this file and save it with the exact structure we reviewed earlier.

Make sure the file is saved with UTF-8 encoding to handle any special characters properly. The placeholders {user_input} and {aspectRatio} are crucial — these allow our code to dynamically insert specific details into the template.

With our template file in place, we now need a way to load it and format it with user input. For this, we'll create a PromptManager class that handles these operations. This class will be responsible for:

1. Loading the template from the file.
2. Inserting user input and settings into the template.
3. Handling any errors that might occur during these operations.

Let's create the PromptManager.php file in the models/ directory:

Let's examine this code in detail:

The loadBasePrompt method:

• Takes an optional $filePath parameter with a default value pointing to our template file.
• Checks if the file exists using file_exists and reads its contents via file_get_contents.
• Returns the contents as a string if successful.
• If an error occurs (e.g., the file doesn't exist), it prints an error message and returns a simplified fallback template containing our placeholders.

The formatPrompt method:

• Takes a $userInput string and an $aspectRatio string.
• Calls loadBasePrompt to get the template.
• Defines an associative array $replacements where keys are the placeholders in the text and values are the actual data.
• Uses str_replace with array_keys and array_values to efficiently replace all placeholders at once.
• Returns the fully formatted prompt string.

The PromptManager class uses static methods, which means they can be called directly on the class without creating an instance. This is appropriate here since we're not storing any state between operations.

Now that we have our template file and PromptManager class, let's create a simple script to test that everything works correctly. We'll create a main.php file in the root of our app/ directory:

This script includes our manager, defines sample data, and prints the result. When you run this script, you should see output similar to the following:

As you can see, our user input and aspect ratio have been successfully inserted into the template. The rest of the template remains unchanged, providing consistent guidance to the AI model.

In this lesson, we built the foundation for our image generation service by designing a reusable and resilient prompt template system. You now have the tools to load a template, insert dynamic content, and gracefully handle errors using PHP.

In the practice session, you'll get hands-on experience modifying templates, trying different inputs, and reinforcing the techniques covered here.

Next, we'll continue building our service by creating an ImageManager class to manage the storage and retrieval of generated images using PHP.

From there, we'll connect this prompt system to the official Gemini API image generation flow using gemini-3.1-flash-image-preview to turn these carefully crafted prompts into actual images. Great job completing the first lesson!