Using AI to Generate Text, Images, and Videos in a Single Workflow.
FROM Module 6: Prompt Engineering: Techniques and Approaches
Introduction
AI is evolving beyond just text-based interactions. Multimodal AI allows users to generate text, images, audio, and videos within a single workflow. This lesson will cover:
✅ What multimodal AI is
✅ Techniques for combining different media types
✅ Real-world applications
✅ Hands-on exercises
What is Multimodal AI?
Definition: Multimodal AI can process and generate content in multiple formats (text, images, video, speech, etc.).
Example:
- You provide a text prompt, and AI generates an image.
- AI then uses the image to generate a descriptive caption or video.
🤖 Popular Multimodal AI Models:
Several advanced AI models can process and generate multiple formats (text, images, video, and speech). Here are some of the top multimodal AI models:
- GPT-4V (Vision) – OpenAI’s multimodal version of GPT-4 that understands images and text together.
- DALL·E 3 – Generates high-quality AI images from text prompts and can now refine images using natural language.
- Gemini 1.5 (Google DeepMind) – Can process text, images, audio, and code in a single model.
- Grok-1.5V (xAI by Elon Musk) – A multimodal version of Grok that can interpret images and text-based inputs.
- Claude 3 (Anthropic) – Capable of handling text and some multimodal tasks (but not as visual-focused as GPT-4V or Gemini).
- Runway Gen-2 – A powerful AI video generator that transforms text prompts into short video clips.
- Pika Labs – Another AI tool for generating animated videos from text descriptions.
- Whisper (OpenAI) – An AI speech-to-text model that accurately transcribes and translates audio.
These models enable seamless multimodal workflows, making it possible to generate, edit, and enhance content across text, images, and video.
Multimodal Prompting Techniques
1. Text-to-Image Generation (Prompting for Images)
AI converts a detailed text prompt into an image.
Example Prompt:
“A futuristic city skyline at sunset, with flying cars and neon holograms reflecting off the glass buildings, in cyberpunk style.”
Best Practices:
- Be descriptive (e.g., “A cozy library with warm lighting and wooden bookshelves.”)
- Specify styles (e.g., “A Van Gogh-style painting of a sunflower field.”)
- Define composition (e.g., “A close-up portrait of a smiling astronaut on Mars.”)
2. Text-to-Video Generation (Prompting for Videos)
AI creates short videos from text descriptions or enhances images into animations.
- Example Prompt for Video AI (Runway ML):
“A golden retriever running on a beach at sunrise, slow motion, cinematic lighting.”
Best Practices:
- Use clear scene descriptions (e.g., “A waterfall in a dense jungle, viewed from a drone.”)
- Define camera movements (e.g., “A slow zoom into a spaceship cockpit.”)
- Add mood settings (e.g., “Dramatic lighting, 4K quality, cinematic tone.”)
Image-to-Text (Descriptive AI Captions & Summaries)
AI analyzes an image and generates text descriptions.
Example Use Case:
- Input: Upload a photo of the Eiffel Tower.
- AI Output: “A stunning view of the Eiffel Tower at night, illuminated against a deep blue sky.”
Best Practices:
- Request detailed descriptions (e.g., “Describe this image in 50 words.”)
- Use contextual instructions (e.g., “Generate a social media caption for this image.”)
4. Text-to-Speech (AI Voice Generation)
AI converts text into realistic voice narration.
Example Prompt for AI Voice:
“Read this article in a warm, friendly voice with natural pauses.”
Best Practices:
- Choose a tone (e.g., “Excited, formal, or calm.”)
- Set a pacing style (e.g., “Slow narration for storytelling.”)
- Specify emotion (e.g., “Sound enthusiastic while describing the product.”)
5. Combining Modalities in a Single Workflow
🔹 Example: AI-Powered Marketing Workflow
1. Generate a product description (Text)
- “A sleek, lightweight smartwatch with 7-day battery life and AI fitness tracking.”
2. Convert it into an ad image (Text-to-Image) - AI generates a high-quality product image.
3. Create a short promo video (Image-to-Video) - AI animates the product with smooth transitions.
4. Add AI voice narration (Text-to-Speech) - A professional AI voice reads the product features.
Best Practices:
- Define the end goal before prompting.
- Use consistent prompts across all media types.
- Fine-tune details to make outputs more realistic.
Real-World Applications of Multimodal AI
1. Content Creation & Marketing
- AI writes blog posts, generates matching images, and creates promotional videos.
- Example: An AI-generated travel blog that includes AI-created images and narrated videos.
2. Virtual Assistants & AI Chatbots
- AI chatbots can answer questions with text and images.
- Example: A virtual home designer suggests furniture and generates room mockups.
3. Art & Design
- AI helps concept artists generate quick sketches before turning them into 3D models.
- Example: Game designers use AI-generated landscapes for virtual worlds.
4. AI-Powered Video Editing
- AI can animate still images into short films.
- Example: Runway AI helps filmmakers create visual effects without green screens.
5. Journalism & Fact-Checking
- AI generates news summaries, verifies images, and detects deepfakes.
- Example: AI scans images to confirm their authenticity in breaking news.
Hands-On Exercise: Create a Multimodal AI Workflow
🔹 Goal: Use different AI tools to generate text, images, and video from a single concept.
Step 1: Generate a Concept
Pick a theme for your multimodal AI project.
- Example: “A futuristic eco-friendly city with AI-powered transportation.”
Step 2: Generate Text Content
🔹 Prompt:
“Write a 100-word description of a futuristic green city powered by AI and renewable energy.”
Step 3: Generate an Image Based on the Text
🔹 Prompt for an AI Image Generator:
“Create a detailed digital artwork of a futuristic eco-city with solar panels, flying cars, and green skyscrapers.”
Step 4: Generate a Short Video from the Image
🔹 Prompt for a Video Generator:
“Animate this futuristic city scene with moving traffic, flying drones, and changing weather effects.”
Step 5: Add AI Voice Narration
🔹 Prompt for AI Voice Generator:
“Narrate this description in an inspiring documentary-style voice.”
✅ End Result: A cohesive AI-generated project combining text, images, video, and speech!
Reflection Questions
- What was the most challenging part of using multimodal AI?
- How did changing the prompts affect AI’s output?
- How could you use multimodal AI in your field (marketing, education, design, etc.)?
The most challenging part of using multimodal AI is that some AI tools can’t create an animated video, but only static picture.
The more ambiguous the prompt, the more complicated the output.
I could use it in marketing content.
The most challenging part was getting the AI to produce exactly what I had in mind. Sometimes small changes in the prompt gave very different results, so I had to keep adjusting my words to get what I wanted.
Changing the prompts made a big difference. When I used clearer or more detailed instructions, the results came out much better. It showed me how important prompt design is in guiding AI to understand what you really want.
Multimodal AI can be used in data and education fields to create engaging content like visual reports, training materials, and data stories. It can make learning and presentations more interactive and easier to understand.
1. The most challenging part of using multimodal AI
One of the trickiest parts is figuring out how to give it the right instructions. Since multimodal AI works with text, images, and sometimes even audio, you have to be really clear on what you want. For example, if you upload a picture and just say, “make this better”, the results might not match what you had in mind. The challenge is balancing detail without overloading the AI. Another issue is that sometimes the AI interprets context differently than a human would, so you need to adjust and refine.
2. How changing prompts affected the output
Changing even a small detail in the prompt can completely change the output. For instance, if you ask for “a professional-looking design” vs. “a fun and colorful design,” you’ll get two very different results. The tone, level of detail, and choice of words matter a lot. It’s kind of like giving instructions to a graphic designer—you need to be specific but also flexible enough to let the AI “interpret” your request.
3. How multimodal AI could be used in your field
Marketing: You could use it to create ad visuals, write captions, and even analyze how people might react to a campaign. For example, generating mockups for social media ads or turning data into easy-to-read infographics.
The most challenging part of using multimodal AI is being able to integrate all the prompts one after the other to achieve the desired output.i.e using text to generate image. Then crafting a image prompt to generate either a video or an audio response. But it was worth the try because I learnt a lot from trying
Changing the prompt resulted to interesting different responses from the model. It made me understand that if a prompt is well crafted I produces a great output. On the other hand, if the prompt isn’t well crafted it simply gives an output that is incorrect.
In the area of IT, I would use multimodal AI to learn about the latest trends and innovations in the IT world. I would also use it to create educational content for aspiring IT enthusiasts.
The challenge I face with multimodal prompting is the AI ability to perfectly curate the scenery and pace them, therefore giving a flawed output. However it is a system that can give flexibility to the output and allows to structure and give more details to your prompt.
How did changing the prompts affect AI’s output ?
Changing the prompts had a big impact on the AI’s output. I realized that even small changes in wording, tone, or structure could lead to very different responses. When I made my prompts more specific and added clearer context, the results became more accurate, relevant, and creative. It taught me that how you ask really determines what you get from the AI.
Safi George
The most challenging part of using multimodal AI for me was learning how to craft prompts that clearly connect both text and images in a way the model understands. At first, it was tricky to figure out how much detail to give or how to guide the AI’s focus when using both visuals and language together. But with Dexa’s clear teaching style and practical examples, I’ve started grasping how to balance clarity and creativity when working with multimodal prompts.
Safi George
Most Challenging Part of Using Multimodal AI
The main challenge is data alignment and integration complexity. It’s hard to get the AI to perfectly synchronize different data types, like text, images, and audio. If they don’t align precisely, the model’s understanding is flawed, leading to inconsistent or unreliable results.
How Did Changing the Prompts Affect AI’s Output?
Prompt changes dramatically shift the output. Being specific is key; adding details or examples of the desired outcome focuses the AI. Even changing the input order (e.g., image first, then text) changes the priority, proving that clear instructions are vital for relevant results.
How Could You Use Multimodal AI in Your Field?
For Marketing: I would use it for efficient social content creation. I could give the AI a product description and brand style examples (images/text), and it would immediately generate a complete, on-brand package ad copy, matching visuals, and even a short promotional video.
Multimodal AI is an artificial intelligence system that can process and integrate multiple types of data (or “modalities”) at the same time, such as text, images, audio, and video.
It essentially allows the AI to combine different “senses” to understand and generate content in a more comprehensive, human-like way.