liveBook · Manning

What you will learn in AI for Everyday IT

Accelerate workplace productivity

by Chrissy LeMaire and Brandon Abshire
Foreword by Nitya Narasimhan

Chapter by chapter

Chapter 1: Artificial intelligence in IT

This introduction frames generative AI as a practical, transformative aid for IT work that must be paired with human judgment and used for real workflow improvements.

1.1 Generative AI changes everything · 1.2 The elephant in the server room · 1.3 Strengthening your skill set · 1.4 An introduction to generative AI · 1.4.1 Language model chatbots · 1.4.2 Image generation with AI models · 1.5 Service costs · 1.6 Using AI responsibly at work · 1.6.1 Data security with AI · 1.6.2 Bias and fairness · 1.6.3 AI writing and plagiarism · 1.6.4 Citing prompts in your work · 1.7 How we are using AI · 1.8 Prompts used in this chapter · 1.9 Summary

What you learn in chapter 1 →

Chapter 2: Chatbots: Tasks and tips

This chapter introduces practical selection and use of AI chatbots and image generators for everyday individual IT work.

2.1 Chatbots and conversational AI · 2.1.1 OpenAI ChatGPT · 2.1.2 Anthropic Claude · 2.1.3 Google Gemini · 2.1.4 Microsoft Copilot suite · 2.1.5 Summary comparison of AI assistants · 2.2 Text-to-image AI models · 2.2.1 OpenAI DALL-E and 4o · 2.2.2 Midjourney · 2.2.3 Google Gemini Imagen and Meta Imagine · 2.3 Prompts used in this chapter · 2.4 Summary

What you learn in chapter 2 →

Chapter 3: Basic intelligence

This introduction previews the basic AI interaction concepts—prompts, statefulness, context, tokens, limits, and account choices—and notes that people use AI through chat interfaces or APIs.

3.1 Defining the terms · 3.1.1 Prompts · 3.1.2 Statefulness · 3.1.3 Context retention and coherence · 3.1.4 Tokens · 3.2 Everything you never wanted to know about tokens · 3.2.1 Token limits · 3.2.2 Token limits and AI interactions · 3.3 When good bots go bad · 3.4 Free vs. premium accounts · 3.4.1 Why pay for chatbot access when I can use one for free? · 3.4.2 Which to choose? · 3.4.3 Learning and experimenting · 3.5 Prompts used in this chapter · 3.6 Summary

What you learn in chapter 3 →

Chapter 4: Prompt engineering and problem formulation

This introduction positions prompt engineering and problem formulation as complementary skills for productive AI interactions and previews their application in the next chapter.

4.1 Prompt engineering · 4.2 The spectrum of AI prompting · 4.2.1 Zero-shot prompting · 4.2.2 Single-shot prompting · 4.2.3 Few-shot prompting · 4.2.4 Many-shot prompting · 4.2.5 The future of prompt engineering · 4.3 The mechanics of a good prompt · 4.3.1 Key principles in prompt construction · 4.4 Introductory prompts for IT roles · 4.4.1 Awesome Prompts Lab · 4.5 Advanced techniques · 4.5.1 Recursive prompts · 4.5.2 Context injection · 4.5.3 Explicit constraints · 4.5.4 Prompt chaining · 4.5.5 Sentiment directives · 4.5.6 Templating · 4.6 Best practices and common mistakes · 4.6.1 Learning from success · 4.6.2 Pitfalls to avoid · 4.6.3 Meta-prompts · 4.7 Problem formulation · 4.8 Incorporating problem formulation · 4.9 Prompts used in this chapter · 4.10 Summary

What you learn in chapter 4 →

Chapter 5: Prompts in action

This introduction previews a project-management demonstration that applies prompt engineering first and problem formulation second, while explaining why a traditional cross-platform prompting approach is used.

5.1 Prompt engineering in action · 5.1.1 Basic prompting · 5.1.2 Recursive prompting · 5.1.3 Template creation · 5.1.4 Finalized output · 5.2 Problem formulation in action · 5.3 Summary

What you learn in chapter 5 →

Chapter 6: Document handling

This chapter introduces using a basic AI chatbot to prepare, summarize, convert, extract, and organize everyday IT documents more effectively.

6.1 Best practices for document handling with AI · 6.1.1 Text extraction methods · 6.1.2 Structuring and refining outputs · 6.2 Ethics in AI document handling · 6.3 Document summarization · 6.4 Format conversion · 6.5 Text extraction from images · 6.6 Document comparison · 6.7 Document classification and tagging · 6.8 Document anonymization · 6.9 Language translation · 6.10 Prompts used in this chapter · 6.11 Summary

What you learn in chapter 6 →

Chapter 7: Emails and instant messaging in the workplace

This chapter introduces how generative AI can make workplace email and instant messaging more efficient through assistance, automation, better writing, and critical judgment.

7.1 Enhancing email management with AI · 7.1.1 AI-enhanced email summarization and drafting · 7.1.2 Not ready for prime time · 7.1.3 Email security · 7.2 Using AI in instant messaging · 7.2.1 Meeting transcription and recap with AI · 7.3 Integrations and automation with AI · 7.3.1 Teams workflows in action · 7.3.2 Webhooks · 7.4 Improving your writing while maintaining your voice · 7.4.1 Grammarly · 7.4.2 Apple Intelligence Writing Tools · 7.5 Maintaining critical thinking and good judgement · 7.6 The future of AI communications · 7.7 Prompts used in this chapter · 7.8 Summary

What you learn in chapter 7 →

Chapter 8: IT support and service desk

This chapter introduction positions the service desk as a user-facing IT function where chatbots can help agents resolve technical issues while supporting users emotionally.

8.1 Service desk therapy · 8.1.1 Identifying problems · 8.1.2 Crafting a response · 8.2 Technical support · 8.2.1 Setting up the environment · 8.2.2 Troubleshooting problems · 8.2.3 Practicing for real-time scenarios · 8.3 Custom GPTs · 8.3.1 Creating a custom GPT to analyze IT service tickets · 8.4 The future of the service desk: Agents · 8.4.1 Key agentic AI offerings · 8.4.2 Agentic AI in enterprise CRM systems · 8.5 Prompts used in this chapter · 8.6 Summary

What you learn in chapter 8 →

Chapter 9: Systems administration

This introduction frames systems administration work and previews how AI tools such as ChatGPT can simplify recurring administrative tasks throughout the chapter.

9.1 Change request · 9.2 More distros, more problems · 9.3 Multiserver administration · 9.4 Renaming files · 9.5 Analyzing error logs: Identifying exceptions and failures · 9.6 Automating tasks for efficiency and compliance · 9.7 Auditing configuration files with AI · 9.8 Existing AI in systems engineering · 9.9 Prompts used in this chapter · 9.10 Summary

What you learn in chapter 9 →

Chapter 10: Database administration and development

This chapter introduction positions AI as a bridge between database development and administration work and previews its use across SQL, optimization, maintenance, and advanced DBA tasks.

10.1 Query and object creation basics · 10.2 Query optimization · 10.3 Code documentation · 10.4 Heterogeneous database environments · 10.4.1 Staying up to date · 10.4.2 Teaching an old DBA new tricks · 10.5 Maintenance jobs · 10.6 Advanced administration · 10.6.1 AI + dbatools = <3 · 10.6.2 Analyzing memory configurations · 10.6.3 Additional advanced administration prompts · 10.6.4 The role of the DBA in the AI era · 10.7 Prompts used in this chapter · 10.8 Summary

What you learn in chapter 10 →

Chapter 11: Code assistants and development tools

This introduction frames AI coding assistants as context-aware development tools, previews their forms and workflow benefits, and sets up privacy and developer expertise as essential considerations.

11.1 Privacy and security · 11.2 Understanding the value of existing skills · 11.3 GitHub AI suite · 11.4 Cline · 11.5 Cursor AI · 11.6 Google Project IDX · 11.7 Aider · 11.8 Summary comparison of code assistants · 11.9 Best practices for AI-assisted development · 11.9.1 Foundation principles · 11.9.2 Advanced techniques · 11.10 Prompts used in this chapter · 11.11 Summary

What you learn in chapter 11 →

Chapter 12: AI in DevOps engineering

This chapter introduces a practical-first approach to incorporating AI into existing DevOps workflows before covering GenAIOps fundamentals.

12.1 Practical AI use cases in DevOps · 12.2 Upgrading nearly 7,000 tests: A real-world AI project · 12.2.1 Choosing our tools · 12.2.2 Developing an effective process · 12.2.3 Implementation strategy · 12.2.4 Managing large files · 12.2.5 Breaking down instructions into focused passes · 12.2.6 The results · 12.2.7 Lessons learned · 12.2.8 Beyond test updates: Other uses for AI-assisted development · 12.3 The GenAIOps lifecycle · 12.3.1 Applying GenAIOps: A practical workshop example · 12.4 Prompts used to write this chapter · 12.5 Summary

What you learn in chapter 12 →

Chapter 13: Building AI-powered applications

This chapter introduces integrating generative AI into applications through APIs, especially function calling that turns model output into structured input for application logic.

13.1 Function calling · 13.1.1 Chatting with the OpenAI API · 13.1.2 Building a database copilot · 13.1.3 Implementing function calling · 13.2 The functions · 13.2.1 What matters in AI function design · 13.3 Validating SQL queries with examine_sql · 13.3.1 Defining what makes a query dangerous · 13.3.2 Generating a user-friendly response · 13.3.3 AI-powered SQL assistance in action · 13.4 Practical applications and security considerations · 13.4.1 Example: AI-powered currency conversion · 13.4.2 Security considerations for function calling · 13.5 Prompts used in this chapter · 13.6 Summary

What you learn in chapter 13 →

Chapter 14: Conflict resolution and crisis management

This chapter introduction presents AI as a practical guide for resolving workplace conflicts, managing IT crises, and supporting disaster recovery while encouraging teams to address problems directly.

14.1 Addressing workplace conflicts · 14.1.1 Strategies for effective conflict resolution · 14.1.2 SharePoint standoff · 14.1.3 Proactive conflict-resolution agreement · 14.2 Crisis management · 14.2.1 Role of leadership in crisis situations · 14.2.2 Generative AI’s role in disaster recovery and COOP · 14.3 Prompts used to write this chapter · 14.4 Summary

What you learn in chapter 14 →

Chapter 15: Management essentials

This chapter introduces AI as a time-saving assistant for routine management work while keeping managers accountable for the human needs of their teams.

15.1 Policies and benefits · 15.1.1 Policies · 15.1.2 Benefits · 15.1.3 Custom GPTs and projects · 15.2 Career development and growth · 15.2.1 Skills matrices · 15.2.2 One-on-ones · 15.3 Performance reviews · 15.3.1 Self-evaluations · 15.3.2 360-degree feedback · 15.3.3 SMART goals · 15.3.4 Team goals · 15.3.5 Manager self-learning · 15.4 Prompts used in this chapter · 15.5 Summary

What you learn in chapter 15 →

Chapter 16: Management interventions

This chapter introduces AI-supported management interventions for sensitive personnel challenges, team cohesion, interviewing, and technical-manager time management while emphasizing human judgment and compassion.

16.1 Management intervention guidelines · 16.1.1 Memos, warnings, and write-ups · 16.1.2 Constructive feedback · 16.1.3 Performance improvement plans · 16.2 Team cohesion · 16.2.1 Anonymous feedback · 16.2.2 Team building · 16.3 Interviewing candidates · 16.4 Time management for the hands-on technical manager · 16.5 Prompts used in this chapter · 16.6 Summary

What you learn in chapter 16 →

Chapter 17: Career advancement

This chapter introduction frames career advancement around using AI to excel in a current IT role and building specialized AI expertise for the future.

17.1 Leading AI adoption in the organization · 17.1.1 Advancing through practical problem-solving · 17.1.2 Documenting and showcasing your projects · 17.1.3 Advanced presentation training with AI · 17.2 Training and certifications · 17.2.1 Acquiring new skills with AI assistance · 17.2.2 Identifying and filling skill gaps with AI · 17.2.3 Writing conference attendance proposals · 17.3 External career advancement · 17.3.1 AI-powered job search · 17.3.2 Resume optimization · 17.3.3 AI-assisted interview preparation · 17.3.4 Salary negotiation · 17.3.5 AI-powered career development prompts · 17.3.6 AI-powered interview practice · 17.4 Ethical considerations and best practices · 17.5 Putting it all together · 17.6 Prompts used in this chapter · 17.7 Summary

What you learn in chapter 17 →

Concepts explained in this book

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