Working Smarter with AI

• AI versus conventional automation: rules, learned patterns and examples from everyday software.
• Machine learning, layered neural networks and language models; token-based generation, context windows and training data at an introductory level.
• Distinguish task capability from human understanding; not all AI is a language-prediction system.
• Explain output variability, missing context, bias and fabricated answers without implying that clear input guarantees reliability.

• Drafting and refining written content
• Summarizing lengthy reports or meeting notes
• Research and information gathering
• Brainstorming and idea generation
• Communication clarity and tone adjustment
• Light data analysis and reporting

Assess sample workflow opportunities and decide where evidence, numerical checks or specialist judgement are essential. Productivity examples are illustrative, not promised results.

• State the task, audience, background and required format; compare instructions, examples and constraints.
• Refine the request iteratively using feedback and concrete acceptance criteria.
• Ask for concise explanations or source-supported conclusions where helpful; do not require hidden chain-of-thought or assume step-by-step prompts improve every model.
• Practise selected tasks: a concise summary, a clearer rewrite, a draft from notes and a small brainstorming exercise.
• Check omissions, unsupported claims, tone and citation validity before retaining the result.
• Save a reusable prompt template together with its required inputs and verification steps.

• Combine AI drafting or idea generation with human revision and structured synthesis.
• Turn sample meeting notes into draft follow-ups, or analytical findings into clear communication; sending or acting requires appropriate permission.
• Agents can plan and invoke tools across steps; a draft/review loop is not necessarily an autonomous agent.
• Evaluate bounded agent demonstrations with permissions, approval points, logs and stop conditions.
• Create a small personal toolkit for planning, check-ins or reflection, and share approved templates with colleagues.

• Recognizing limitations: hallucinations, bias, context gaps
• Data responsibility: what not to share with AI systems
• Verifying factual accuracy
• Maintaining confidentiality and professional standards
• Ethical use: transparency, citation, and fairness

Apply organisational rules to data access and disclosure; check current tool settings rather than assuming every account has the same protections.

Select one repeatable task, define quality and effort measures, and retain human accountability for its outputs.