Overview
You know AI tools exist—now learn to use them like a professional. This two-day course transforms you from someone who occasionally tries ChatGPT into someone who consistently gets reliable, high-quality results from AI assistants across your entire workflow.
You’ll master the fundamentals of effective prompt construction, learn platform-specific techniques for ChatGPT, Claude, Gemini, and Copilot, and develop reusable prompt frameworks for your most common tasks. Whether you’re drafting client communications, analyzing data, conducting research, or automating repetitive writing tasks, this course gives you the skills to integrate AI assistants confidently and effectively into your professional work.
Format: 2 Days | VILT
Who Should Attend
- Business professionals looking to leverage AI for productivity gains
- Content creators, marketers, and communications specialists
- Analysts and researchers working with large amounts of information
- Managers seeking to implement AI tools across their teams
- Anyone frustrated with inconsistent or low-quality AI outputs
What You’ll Learn
- Apply proven prompt engineering principles for consistent, high-quality outputs
- Choose the right AI platform for specific tasks and understand their strengths
- Use advanced techniques like chain-of-thought, few-shot learning, and role-based prompts
- Build reusable prompt templates and libraries for common professional tasks
- Implement AI assistants for writing, analysis, research, and decision support
- Recognize AI limitations, avoid common pitfalls, and maintain quality control
Course Outline
Module 1: Prompt Engineering Fundamentals
- How large language models actually work: tokens, context windows, and temperature
- The anatomy of an effective prompt: clarity, context, constraints, and examples
- Common mistakes that produce poor results and how to fix them
- Understanding AI capabilities and limitations across different platforms
- When to use which AI: ChatGPT vs. Claude vs. Gemini vs. Copilot
Module 2: Core Prompting Techniques
- Zero-shot vs. few-shot prompting: when and how to provide examples
- Role-based prompts: assigning expertise and perspective
- Chain-of-thought reasoning: getting AI to show its work
- Iterative refinement: improving outputs through conversation
- Output formatting: structured responses, tables, JSON, and custom formats
- Prompt chaining: breaking complex tasks into sequential steps
Module 3: Professional Writing and Communication
- Emails, reports, and business correspondence: maintaining your voice and tone
- Marketing copy, social media, and persuasive content
- Technical documentation and process descriptions
- Editing and revision workflows: using AI as a collaborative partner
- Adapting content for different audiences and contexts
- Quality control: when to accept, edit, or reject AI suggestions
Module 4: Research, Analysis, and Data Work
- Research synthesis: summarizing sources and extracting key insights
- Competitive analysis and market research applications
- Data interpretation and explaining complex information clearly
- Creating frameworks, taxonomies, and organizational structures
- Fact-checking AI outputs and verifying information accuracy
- Combining AI research with web search and traditional methods
Module 5: Advanced Prompt Engineering Strategies
- Custom instructions and system prompts for consistent behavior
- Building prompt templates and libraries for recurring tasks
- Metacognitive prompting: teaching AI to evaluate its own responses
- Negative prompting: explicitly defining what you don’t want
- Context management: working within token limits for long conversations
- Multi-turn conversations: maintaining context and building on previous outputs
Module 6: Workflow Integration and Team Implementation
- Identifying high-value use cases in your specific role or industry
- Creating standard operating procedures for team AI use
- Building shared prompt libraries and best practices
- Privacy, security, and confidentiality considerations
- Measuring productivity gains and ROI from AI integration
- Training team members and overcoming resistance
Module 7: Ethics, Limitations, and Quality Control
- Recognizing hallucinations, bias, and factual errors
- Disclosure and transparency: when to credit AI assistance
- Copyright, plagiarism, and intellectual property considerations
- Developing critical evaluation skills for AI outputs
- Human-in-the-loop workflows: maintaining professional judgment
- Staying current as AI capabilities evolve
Prerequisites & Technical Requirements
- Basic familiarity with at least one AI assistant (ChatGPT, Claude, Gemini, or Copilot)
- Active accounts for AI platforms you wish to use (free tiers acceptable)
- Understanding of your own professional workflows and common tasks
- No coding or technical background required
Customization Options
This course can be tailored to your industry or role—marketing and content creation, data analysis and research, customer service and communications, or technical documentation. We can focus on specific AI platforms your organization uses and incorporate your actual work tasks for hands-on practice with real-world applications that deliver immediate value.