AI at Work

AI at Work

From Understanding to Execution - 1 day

Build practical AI fluency for modern work, from first principles to prompts, context, agents, and the tools reshaping productivity.

AI is no longer a peripheral tool, it is becoming a working layer across writing, research, analysis, coding, operations, and decision-making. What matters today is not just understanding what AI is, but knowing how to use it effectively within real workflows.

This course focuses on building that capability: a clear conceptual foundation, practical prompting skills, deeper context design, and an informed view of modern tools such as ChatGPT, Claude, Gemini, Manus, OpenClaw, Gemini CLI, and MCP.

The course is led by an instructor with over 30 years of industry experience and is grounded in real industry-demanded content rather than academic theory.

Learning Outcomes

By the end of this course, participants will be able to:

• Explain what AI is in clear, practical terms
• Explain neural networks in simple, non-technical language
• Write effective prompts for different business use cases
• Differentiate between prompt engineering and context engineering
• Structure context to improve AI output quality and reliability
• Understand what agentic AI is and how it differs from chat-based interaction
• Evaluate modern AI tools and platforms for productivity
• Explain the role of MCP in connecting AI to tools and data
• Identify risks, limitations, and governance considerations in AI usage

Prerequisites

• Basic digital literacy
• Familiarity with common workplace tasks such as writing, research, reporting, or communication
• No programming background required
• No prior AI knowledge required

Detailed Training Outline

1. AI fundamentals
   1. What artificial intelligence is
   2. Types of AI
   3. How AI works (non-technical view)
   4. Data and pattern learning
   5. Models and training vs inference
   6. Tokens and outputs
   7. Probability and prediction
   8. Context windows and memory
   9. Neural networks explained simply
2. Large language models in practice
   1. What LLMs are designed to do
   2. Strengths
      1. Drafting
      2. Summarization
      3. Transformation
      4. Ideation
      5. Analysis
   3. Limitations
      1. Hallucination
      2. Inconsistency
      3. Lack of domain grounding
   4. Managing reliability
3. Prompt engineering
   1. What prompt engineering is
   2. Structure of effective prompts
      1. Objective
      2. Task
      3. Context
      4. Constraints
      5. Output format
   3. Prompt patterns
      1. Summarization
      2. Drafting
      3. Extraction
      4. Planning
      5. Comparison
   4. Improving prompt quality
      1. Clarity
      2. Specificity
      3. Task decomposition
      4. Output structuring
   5. Common mistakes
4. Context engineering
   1. What context engineering is
   2. Prompt vs context engineering
   3. Components of context
      1. Instructions
      2. Examples
      3. Documents
      4. Memory
      5. Tools
      6. Constraints
   4. Designing effective context
      1. Relevance
      2. Accuracy
      3. Brevity
      4. Consistency
   5. Context for teams and organizations
      1. Shared knowledge
      2. Templates
      3. Connected systems
   6. Failure modes
5. AI for productivity
   1. Augmentation vs automation
   2. Chat vs workflows
   3. Individual vs team productivity
   4. Use cases
      1. Writing
      2. Research
      3. Planning
      4. Communication
      5. Knowledge work
6. Agentic AI
   1. Definition and characteristics
      1. Goal-driven execution
      2. Tool usage
      3. Multi-step workflows
   2. Agents vs chatbots
   3. Where agents are useful
   4. Risks and controls
7. Manus
   1. Positioning as an execution-focused AI system
   2. Task automation and workflow completion
   3. Role in the agentic ecosystem
   4. Evaluation considerations
8. OpenClaw VS NemoClaw
   1. Open agent framework concepts
   2. Always-on assistants
   3. Personal and enterprise implications
   4. Security and governance considerations
9. Gemini CLI
   1. AI in the command line
   2. File and task execution
   3. Workflow automation
   4. Use of connected tools and services
10. General AI assistants
    1. ChatGPT
    2. Claude
    3. Gemini
    4. Comparing capabilities and use cases
    5. Choosing the right tool for the task
11. MCP (Model Context Protocol)
    1. Purpose and concept
    2. Connecting AI to tools and data
    3. Standardization and interoperability
    4. Role in modern AI systems
12. Responsible AI usage
    1. Privacy and data handling
    2. Hallucination management
    3. Human oversight
    4. Governance and compliance
13. Organizational adoption
    1. Identifying high-value use cases
    2. Designing workflows
    3. Scaling usage
    4. Managing risk
    5. Building internal capability
14. Closing integration
    1. Connecting concepts: AI, prompts, context, tools, agents
    2. Evaluating new AI tools
    3. Building a practical AI adoption mindset