Multimodal Prompt Engineering Training is a practical two-day program designed to help learners master the art and science of prompting multimodal AI systems that work across text, images, audio, and video. This course focuses on designing effective prompts for multimodal understanding, generation, reasoning, and interaction, enabling participants to build reliable, high-quality AI outputs for creative, business, and technical use cases.

• Understand multimodal prompt engineering concepts
• Learn prompting for text, image, audio, and video AI
• Understand AI response optimization techniques
• Gain knowledge of structured prompting methods
• Learn context and instruction design basics
• Understand AI workflow automation concepts
• Explore enterprise prompt engineering use cases
• Identify responsible AI prompting practices

• AI and machine learning professionals
• Prompt engineers and AI practitioners
• Product managers working with multimodal AI systems
• Content, design, and creative professionals
• Technology professionals adopting generative AI

• Basic understanding of artificial intelligence or generative AI concepts
• Familiarity with text-based prompting tools is beneficial
• Awareness of different content modalities such as images or audio
• Interest in advanced AI interaction techniques

• Designing effective prompts for multimodal AI systems
• Managing context across text, image, and audio inputs
• Improving output quality, consistency, and reliability
• Applying multimodal prompting for real-world use cases
• Evaluating ethical and responsible prompting practices

By the end of this training, participants will be able to design, test, and refine effective prompts for multimodal AI systems, improve output quality across different modalities, apply advanced prompting techniques for real-world applications, and follow ethical and responsible AI interaction practices.

Foundations of Multimodal Prompt Engineering
• Understanding multimodal AI capabilities and limitations
• Difference between text-only and multimodal prompting
• Prompt structures and components for multimodal systems
• Common multimodal prompting patterns

Prompting with Text and Images
• Designing prompts for image understanding and description
• Combining text instructions with visual inputs
• Controlling style, tone, and detail in image outputs
• Iterative refinement of text-image prompts

Context and Reasoning Across Modalities
• Managing context windows and memory
• Multistep reasoning using multimodal inputs
• Handling ambiguity and incomplete inputs
• Reducing hallucinations in multimodal outputs

Prompting with Audio and Video Inputs
• Prompting for audio transcription and understanding
• Generating insights from audio and video content
• Designing prompts for multimodal summaries
• Synchronizing context across multiple media types

Advanced Multimodal Prompt Patterns
• Instruction chaining and task decomposition
• Role-based and system-level prompting
• Evaluation and debugging of multimodal prompts
• Performance optimization techniques

Ethics, Safety, and Best Practices
• Responsible prompting and bias mitigation
• Copyright and data usage considerations
• Safety risks in multimodal content generation
• Establishing prompt governance and standards

• Multimodal AI fundamentals
• Prompt engineering concepts
• Text and image prompting techniques
• Audio and video AI prompting
• Structured prompt design
• Context optimization methods
• AI workflow automation basics
• Enterprise AI use cases
• Ethical AI prompting practices
• Practical prompt engineering scenarios

• Hands-on multimodal prompt design exercises
• Prompt optimization and quality assessment
• Multimodal use case mini project
• Final knowledge evaluation quiz

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Participants who successfully complete the training will receive an AcadNXT Certification in Multimodal Prompt Engineering Training, validating their expertise in designing effective prompts for multimodal AI systems and real-world applications.