🚀 Generative AI Interview Questions with Answers — Part 7
🎨 Text, Image, and Multimodal Generation
61. What is text generation?
Text generation is the process where AI models create human-like text based on a given input or prompt.
Examples:
• Chatbots
• Article writing
• Email drafting
• Code generation
• Story writing
LLMs generate text token by token using probability prediction.
Popular text generation models include:
• ChatGPT
• Claude
• Gemini
62. What is image generation?
Image generation is the process of creating images using AI models from text prompts or other inputs.
Example Prompt:
“A futuristic city at night with neon lights.”
AI image models can generate:
• Art
• Photorealistic images
• Logos
• Illustrations
• Product designs
Popular image generation tools:
• DALL·E
• Midjourney
• Stable Diffusion
63. What are diffusion models?
Diffusion models are AI models used mainly for image generation.
They work by:
1. Adding noise to images during training
2. Learning how to remove that noise
3. Generating new images step by step
Diffusion models are known for:
• High-quality image generation
• Realistic visuals
• Better artistic control
Most modern AI image generators use diffusion architectures.
64. How do diffusion models work at a high level?
At a high level, diffusion models work in two phases:
Training Phase:
• Noise is gradually added to images
• Model learns how to reverse the noise process
Generation Phase:
• Start with random noise
• Gradually remove noise
• Final image emerges step by step
This iterative denoising process creates highly realistic images.
65. What is multimodal AI?
Multimodal AI refers to systems that can understand and generate multiple data types together.
Examples of modalities:
• Text
• Images
• Audio
• Video
• Documents
Example: An AI that can:
• Read an image
• Understand text
• Answer questions about the image
Multimodal systems are becoming increasingly important in modern AI.
66. How do text and image models work together?
Text and image models work together by connecting language understanding with visual understanding.
Workflow:
1. Text prompt is converted into embeddings
2. Image model interprets the embeddings
3. AI generates or analyzes images based on text meaning
Example: Prompt:
“A cat wearing sunglasses on a beach.”
The text encoder guides the image generation model.
67. What is image-to-text generation?
Image-to-text generation means converting visual information into text descriptions.
Examples:
• Image captioning
• OCR systems
• Visual question answering
• Accessibility tools
Example: Input: 📷 Image of a dog playing in a park
Output:
“A brown dog running in a grassy park.”
This technology helps visually impaired users and powers many AI assistants.
68. What is text-to-image generation?
Text-to-image generation creates images from natural language prompts.
Example: Prompt:
“A cyberpunk city during rainfall.”
The AI interprets the prompt and generates matching visuals.
Applications:
• Marketing
• Gaming
• Design
• Animation
• Advertising
• Content creation
Text-to-image systems became extremely popular with tools like Midjourney and DALL·E.
69. What is cross-modal generation?
Cross-modal generation means generating one type of data from another modality.
Examples:
• Text → Image
• Image → Text
• Text → Audio
• Audio → Text
• Video → Text
Example: A prompt generates:
• An image
• A song
• A video narration
Cross-modal AI enables richer interactive systems.
