Module 4 - Total

• Module 4 focuses on tailoring prompting strategies for various AI models.
• The goal is to maximize each model's potential for business, creativity, or technical applications.
• The module helps customize prompts for efficiency and precision across different AI models.

Understanding AI Model Differences

• Different AI models are like unique tools, each designed for a specific function.
• Understanding their design ensures using the right tool for the job.

Types of Models

• Text-based models (GPT-4, Claude, Gemini) are designed for writing and conversation.
• Image generation models (DALL·E, MidJourney) are useful for digital art.
• Code-focused models (Codex, Copilot) are for programming support.

Impact of Architecture and Training Data

• Each AI model is trained on a specific dataset, shaping its capabilities and limitations.
• GPT-4 excels in language-based tasks because of natural language training.
• DALL·E specializes in turning descriptive prompts into creative visuals.

Strengths by Use Case

• GPT-4 is ideal for creating long-form content, research summaries, or blog drafts.
• Codex is best for generating Python scripts or automating repetitive programming tasks.
• DALL·E thrives when tasked with creative and aesthetic-driven visuals.

Real-World Example

• In an AI-generated marketing campaign, GPT-4 handles copy, while MidJourney creates visuals.
• Blending these tools ensures a winning combination.

Optimizing Prompts for Text-Based AI Models

• Use text-based models like GPT-4, Claude, and Gemini.

Best Practices

• Being specific in prompts is crucial.
• A weak prompt is:"Tell me about marketing."
• A strong prompt is: "List three emerging digital marketing strategies for startups in 2024."
• Adjust style and tone, e.g., “Rewrite this paragraph in a casual tone with a sense of humor.”
• Leveraging context helps AI, so include details like audience, format, and intent.

Applications

• For research, prompt: “Summarize key findings of the latest AI trends and provide citations.”
• For customer service, prompt: “Write an empathetic response to a customer asking for a refund.”
• For content creation, prompt: “Draft a 5-paragraph blog post about the benefits of remote work.”

Tailoring Prompts for Image and Code Generation Models

• Use AI like DALL·E for visuals and tools like Codex for coding tasks.

Image Generators

• Set the scene vividly - instead of “Create a sunset,” try, “Generate a golden sunset over a serene lake, with birds flying in the distance.”
• Define the output needed; for brand visuals, prompt: “Design an abstract logo in shades of teal and gold.”

Code AI Tools

• Precision matters in prompts.
• A weak prompt is: “Help me code a form.”
• A strong prompt is: “Write a JavaScript function that creates a sign-up form with fields for name, email, and a password field with validation.”
• Iterate prompts for quality, and refine outputs.

Enhancing Responses with System Instructions

• System instructions tell AI how to behave or format output.

Techniques

• Set expectations like: “Respond as a professional legal advisor.”
• Specify formatting like: “Write in bullet points, and limit the response to 200 words.”
• Control complexity like: “Provide a beginner-friendly explanation of blockchain, using analogies.”

Benefits

• System instructions add consistency for professional outputs.

Comparing AI Outputs Across Models

• Testing multiple models identifies the best fit.

Steps

• Run the same prompts on GPT-4, Claude, and Gemini.
• Evaluate outputs for clarity, creativity, and relevance.
• Combine strengths by using different models for specific tasks.

Example Workflow

• In an ad campaign, GPT-4 creates copy, DALL·E generates visuals, and Codex automates reporting.