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 parts of using multimodal AI generally revolve around several areas:
* Data Integration and Synchronization:
* Heterogeneous Data: Multimodal AI deals with vastly different data types (e.g., text, images, audio, video, sensor data), each with its own structure, format, and characteristics. Combining these disparate data streams effectively is a major hurdle.
* Alignment: Precisely aligning data from different modalities is crucial. For instance, in video analysis, audio needs to be perfectly synchronized with visual frames. Misalignment can lead to incorrect interpretations and degraded performance.
* Data Quality and Consistency: Ensuring consistent quality across all modalities is difficult. One modality might have high-quality, well-labeled data, while another is noisy, incomplete, or poorly labeled. This can propagate errors throughout the model.
* Missing Data: Real-world datasets often have missing information in one or more modalities. Developing models that can robustly handle partial data and infer missing information is a significant challenge.
* Computational and Memory Demands:
* Resource Intensity: Multimodal models are inherently more complex and require significantly more computational power and memory than single-modality systems. Training and deploying these models demand specialized hardware (like GPUs or TPUs) and robust infrastructure.
* Scaling: Scaling multimodal systems for real-time processing or large-scale applications (e.g., autonomous vehicles) is challenging due to the high computational load and need for efficient parallel processing.
* Model Complexity and Design:
* Architecture Design: Designing effective architectures that can learn meaningful representations from multiple modalities and integrate them cohesively is a complex task. This often involves intricate neural networks and specialized fusion techniques.
* Fusion Strategies: Deciding how and at what stage to fuse information from different modalities (early, late, or hybrid fusion) impacts model performance and interpretability.
* Interpretability: As multimodal models become more complex, understanding why a model makes a particular prediction (i.e., its interpretability) becomes more difficult, especially when multiple modalities contribute to the decision.
* Bias and Fairness:
* Propagated Biases: Biases present in the training data of one modality can easily propagate to other modalities, leading to skewed or discriminatory outcomes in the multimodal model’s predictions.
* Ensuring Fairness: Ensuring fairness and reducing bias across diverse data sources requires careful calibration and continuous monitoring, which adds to the complexity of development and deployment.
* Lack of Standardized Datasets and Benchmarks:
* Data Availability: While large amounts of unimodal data exist, high-quality, diverse, and well-aligned multimodal datasets are often scarce, especially for specialized applications or rare languages.
* Evaluation: Evaluating the performance of multimodal AI models can be more complex than single-modality models, as there might not be a single “correct” answer, and subjective assessments may be required for tasks like image or video captioning.
Q2. Changing prompts has a profound and often dramatic impact on an AI’s output. This is the core principle behind prompt engineering, a rapidly developing field focused on crafting inputs (prompts) to guide AI models toward generating desired, accurate, and relevant outputs.
Here’s a breakdown of how changing prompts affects AI’s output:
1. Content and Topic:
* Specificity: A vague prompt like “Tell me about cars” will result in a general overview. A specific prompt like “Explain the advancements in electric vehicle battery technology in the last decade” will yield a focused and detailed response.
* Keywords: Including specific keywords or phrases in your prompt will direct the AI’s attention to those concepts, influencing the information it retrieves and generates.
2. Tone and Style:
* Formal vs. Informal: Prompts can dictate the tone. “Write a formal business proposal” will produce a different style than “Write a friendly email to a colleague.”
* Creative vs. Factual: Asking for a “creative story about a dragon” will encourage imaginative language, while “List the known species of dragons in mythology” will solicit a factual, concise output.
* Persona: You can instruct the AI to adopt a specific persona, e.g., “Act as a seasoned travel guide and describe the Eiffel Tower.” This will influence the vocabulary, perspective, and overall feel of the response.
3. Format and Structure:
* Lists, Paragraphs, Code: Prompts can specify the desired output format. “Give me a bulleted list of tips,” “Write a 500-word essay,” or “Generate Python code for a simple calculator” all guide the AI to structure its response accordingly.
* Length: You can request specific word counts, paragraph limits, or general conciseness/verbosity.
* Headings and Subheadings: Explicitly asking for headings or a particular structure will ensure a well-organized output.
4. Accuracy and Relevance:
* Context: Providing more context helps the AI understand your intent better, leading to more accurate and relevant responses. For example, instead of just “Summarize this document,” you might say, “Summarize this legal document, focusing on the key liabilities.”
* Constraints and Guidelines: Setting boundaries (“Do not mention X,” “Only use information from Y source,” “Keep it under 100 words”) helps the AI stay on track and avoids irrelevant or undesirable content.
* Examples (Few-Shot Learning): Providing a few examples of desired input-output pairs (e.g., “Translate ‘Hello’ to ‘Hola’, ‘Goodbye’ to ‘Adios’, now translate ‘Thank you'”) can dramatically improve the AI’s ability to follow complex patterns and generate consistent output.
5. Nuance and Depth:
* Level of Detail: “Explain quantum physics to a child” will be very different from “Explain quantum physics to a graduate student.” The prompt dictates the depth of explanation.
* Chain-of-Thought Prompting: Asking the AI to “think step-by-step” or “explain its reasoning” can lead to more logical, detailed, and accurate outputs, particularly for complex problems.
Why is this so impactful?
AI models, especially large language models (LLMs), are trained on massive datasets and learn patterns in language. They don’t “understand” in the human sense, but rather predict the next most probable word or sequence of words based on the input they receive. By changing the prompt, you are essentially:
* Shifting the probability distribution: You’re steering the AI towards a specific subset of its learned knowledge.
* Providing a new starting point: The prompt acts as the initial context, influencing the entire generation process that follows.
* Activating specific “neurons” or pathways: Different words and phrases activate different parts of the AI’s complex internal representation, leading to varied outputs.
In essence, prompt engineering is the art and science of communicating effectively with AI. A well-crafted prompt can unlock an AI’s full potential, transforming a generic response into a highly tailored, valuable, and precise output.
Q3. Multimodal AI could revolutionize various aspects of construction work, making it safer, more efficient, and more precise. Here’s how I, as a construction worker, could leverage multimodal AI:
* Enhanced Safety Monitoring and Hazard Detection:
* Visual (Cameras/Drones) + Audio (Microphones) + Thermal (IR Cameras):
* Real-time Hazard Identification: AI could continuously analyze video feeds from site cameras and drones to identify workers not wearing proper PPE (helmets, vests, gloves), detect unsafe acts (e.g., working at heights without fall protection), or spot unauthorized personnel in restricted areas.
* Equipment Malfunction Detection: Acoustic analysis could identify abnormal sounds from machinery (e.g., grinding, squealing) indicating potential mechanical failures, while thermal cameras could spot overheating components before they fail.
* Proximity Warnings: Combining visual detection of workers and heavy machinery with GPS data could trigger automated warnings to operators and workers if they get too close to dangerous equipment.
* Gas Leak Detection: Integrating sensor data for specific gases (e.g., methane, carbon monoxide) with visual cues in confined spaces could provide immediate alerts.
* Quality Control and Progress Tracking:
* Visual (3D Scans/Photos) + Lidar + Blueprints (Digital):
* Automated Quality Checks: AI could compare 3D scans of newly laid concrete or erected structures against digital blueprints and BIM models to identify deviations from specifications (e.g., incorrect dimensions, misaligned rebar, uneven surfaces) in real-time.
* Progress Monitoring: Drones equipped with cameras and LiDAR could autonomously survey the site daily, and AI could analyze the collected data to track construction progress, compare it against the schedule, and identify potential delays or areas where work is lagging.
* Material Verification: AI could identify and count delivered materials, ensuring they match the order and specifications, reducing errors and waste.
* Predictive Maintenance of Equipment:
* Vibration Sensors + Thermal Cameras + Audio Analysis + Historical Data:
* Early Anomaly Detection: AI could analyze vibration patterns, temperature changes, and subtle sounds from heavy machinery (cranes, excavators, generators) to predict equipment failures before they occur. This allows for proactive maintenance, reducing downtime and costly repairs.
* Fuel and Performance Optimization: By combining sensor data on engine performance, fuel consumption, and operational patterns, AI could recommend optimal usage strategies or identify inefficiencies.
* Optimized Material Management and Logistics:
* RFID Tags + GPS + Visual Recognition:
* Automated Inventory Management: AI could track the location and quantity of materials on site, linking RFID-tagged materials with their visual appearance. This would prevent loss, optimize storage, and ensure materials are available when needed.
* Traffic Management on Site: Visual analysis of vehicle movements combined with GPS could optimize traffic flow on large construction sites, reducing congestion and improving safety.
* Enhanced Training and Skill Development:
* Virtual Reality (VR) + Haptic Feedback + Biometric Data:
* Immersive Training: AI-powered VR simulations could provide realistic training scenarios for operating complex machinery or performing high-risk tasks, allowing workers to practice in a safe environment.
* Performance Feedback: Biometric data (e.g., eye-tracking, body posture) combined with visual analysis within VR could provide personalized feedback on a worker’s technique, helping them improve their skills faster.
* Real-time Documentation and Reporting:
* Voice-to-Text + Visual (Photos/Videos) + Geotagging:
* Automated Reporting: Workers could simply describe observations or issues verbally, and AI would convert it to text, categorize it, geotag it, and attach relevant photos or videos for instant, comprehensive reporting, streamlining communication and reducing paperwork.
By integrating these diverse data streams and allowing AI to learn from their interplay, we could move towards a truly “smart” construction site where safety, efficiency, and quality are continuously monitored and optimized.
1. THE MOST CHALLENGING USE OF MULTIMODAL AI
✓ Aligning Inputs with Intent – Multimodal AI can process text, images, and even data simultaneously, but clarity of intent is still critical. It’s challenging to frame inputs (like combining an image of a project site with a request for a risk analysis) in a way the AI fully understands.
✓ Interpreting Visuals Accurately – While the AI can analyze maps, charts, or diagrams, it sometimes misses nuanced elements—such as contextual factors in infrastructure planning, cultural implications, or regulatory restrictions—that a human would immediately catch.
✓ Prompt Complexity – Creating prompts that meaningfully link multiple modes (e.g., “Based on this feasibility study chart and the project brief, generate a risk mitigation plan”) can be tricky. It requires thinking like a designer, analyst, and communicator all at once.
✓ Data Privacy & Sensitivity – Sharing visual data like site plans, financial charts, or stakeholder presentation decks with AI tools must be handled carefully, especially in sensitive projects.
2. THE IMPACT OF CHANGING AI PROMPTS ON OUTPUT
✓ Tone and Audience Shift – Small changes like saying “Explain to a policymaker” vs. “Summarize for a technical advisor” dramatically alter the tone, terminology, and complexity of the response.
✓ Scope and Depth – Adding or removing context (e.g., “Focus on urban infrastructure in West Africa”) affects how detailed or focused the output is. Specific prompts lead to sharper, more relevant answers.
✓ Creativity vs. Precision – Prompts that are open-ended (e.g., “Give innovative PPP models”) lead to more creative outputs, while highly specific prompts (e.g., “List five risk-sharing mechanisms for toll road PPPs”) narrow the response to facts.
✓ Multimodal Fusion – In multimodal settings, asking AI to interpret versus summarize an image or chart can lead to very different outputs—one being descriptive, the other analytical.
3. THE USE OF MULTIMODAL AI IN PPPs
✓ Feasibility Analysis Support – Upload maps, graphs, or site images along with text prompts to generate visual summaries, assess environmental risks, or brainstorm design solutions.
✓ Visual Reporting – Convert data-heavy documents into AI-assisted infographics, slide decks, or simplified visuals for stakeholders who may not be technical experts.
✓ Stakeholder Communication – Create multimodal simulations (e.g., combine narrative + visuals) for market sounding sessions or public consultations, making information more engaging and accessible.
✓ Training Modules – Design interactive, AI-powered training content using images, flowcharts, and text to explain PPP lifecycle stages, contract structures, or risk allocation.
✓ Contract & Document Review – Combine scanned legal docs, summary notes, and your own comments to let AI assist in flagging inconsistencies or simplifying complex contractual language for different audiences.
The biggest challenge is usually craft the prompt but after a few try outs everything goes well, I mostly find myself zero- shot promoting 😅
Being iterative always brings me closer to desired results and exposes the thought process to different perspectives.
In research, giving context and adding a pesona while prompting has been especially rewarding.
I learnt from this hands-on exercise on how to implement a multimedia workforce into my job as a virtual assistant and social media marketer. The challenges I faced was crafting consistent prompt that would yield the desired results across all media types.
(Comment 1). The most challenging part of using Multimodal AI, is to input the right prompt to align and integrate with what you are creating. Creating a workflow that needs to generate an output that involves text, image, video, audio all aligned together needs accuracy in the tight prompt input.
(Comment 2). Changing the prompts affects AI output by generating accurate reflections of the prompt giving to it for its output.
(Comment 3). I can use it for a more articulative presentations, safety at work illustrations with vivid animations and message.
This module was incredibly insightful! I was especially fascinated by how different AI models like GPT-4V, DALL·E 3, and Runway Gen-2 can work together to create seamless multimodal workflows. The hands-on exercise helped me understand how combining text, images, video, and voice can be powerful for storytelling or marketing. I’d love to explore how this can be applied in education or digital media. The biggest challenge for me was crafting detailed prompts that could translate well across formats. Looking forward to experimenting more with these tools!
Very good to uni
It was amazing how someone can use AI models to generate images, animate the image,put voice over using AI and turn it into film… really amazing
It was amazing