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ChatGPT

As explained in AI for Everyday IT

ChatGPT is OpenAI's conversational generative AI service for interacting with users through natural-language prompts and responses.

ChatGPT is OpenAI’s conversational generative AI service: a language model, chatbot, and AI system that responds to natural-language prompts. It helps IT professionals research, write, analyze, troubleshoot, and automate routine work, while human expertise remains necessary for asking useful questions and checking the results. section 1.4.1 section 1.0

Use ChatGPT across practical IT and workplace tasks:

  • Research current information through web search, with citations that support verification and further reading. section 2.1.1
  • Analyze uploaded files, images, logs, and service-desk data to identify errors, patterns, and possible resolutions. section 8.2.2 section 9.5
  • Create practical work products such as change requests, documentation, emails, learning plans, and conflict-resolution agreements. section 9.0 section 14.1.3
  • Generate or improve scripts for repetitive administration, including checking kernel versions across multiple servers. section 9.3

Troubleshoot VPN connection drops

Attach a CSV export of service-desk tickets and ask: “Based on the information in the file, provide the most common resolutions to problems with VPN connection drops.” ChatGPT groups recurring remedies, such as adjusting VPN settings, updating the client, switching servers, and resetting credentials. Validate each recommendation against the actual environment before applying it. section 8.2.2

Analyze an error log

Upload a log and ask: “Read the uploaded SharePoint log file and extract all lines containing error, exception, failure, or fatal messages. Then tell me what went wrong.” Review the diagnosis and protect sensitive data by using an enterprise provider or removing IP addresses, hostnames, and usernames. section 9.5

Table 1.1 Glossary of AI terms
Term
Definition
Example
AI
Software that mimics human thinking and learning
OpenAI ChatGPT, Apple Siri, Tesla Autopilot, Amazon Alexa, Google Assistant
AI apps
Programs that use AI to handle specific tasks
Google Maps for navigation, Netflix and Spotify for recommendations, Zoom for noise reduction
AI model
Software trained on data to spot patterns and make decisions
OpenAI GPT-4, Tesla Full Self-Driving (FSD) AI, Stable Diffusion
API
Rules that let software systems talk to each other; used to add AI features to programs
OpenAI API, Anthropic API, Google Cloud AI API, AWS Bedrock API, Azure AI Services API
Chatbot
AI program that talks with users to help or provide information
OpenAI ChatGPT, Anthropic Claude (Sonnet frontend)
Cloud-hosted AI
AI systems running on internet servers that are easy to use and scale
OpenAI API, Azure AI services, AWS AI services, Google AI services
Generative AI
AI that creates new content by learning from existing data
DALL-E, Stable Diffusion, Midjourney, ChatGPT, Google Imagen
LLMs
Big AI systems trained on lots of text that can understand and write like humans
OpenAI GPT-4, Google Gemini, Anthropic Sonnet, Meta Llama
Local LLM
AI that runs on your own computers for better privacy and control
Meta Llama, Mistral AI, Microsoft Phi, Black Forest Labs FLUX
SLMs (small language models)
Smaller AI models that work well with limited resources
OpenAI GPT-4o mini, Microsoft Phi, Mistral Small

The glossary distinguishes ChatGPT’s related roles as AI, AI model, chatbot, generative system, API-connected service, and cloud-hosted AI. section 1.4.1

How AI for Everyday IT covers it

How deep it goes
Explains capabilities through workplace examples and prompts, but defers model internals and requires human verification of outputs.
Examples
Uses web research, VPN-ticket analysis, SharePoint-log diagnosis, Bash scripting, change requests, and conflict-resolution agreements.
What it assumes
Assumes basic IT concepts and teaches AI terminology, prompting practices, practical workflows, security, ethics, and citation awareness.
What you can do after
Apply ChatGPT to research, analyze files, draft work products, troubleshoot systems, and automate repetitive IT tasks while validating results.
How it connects
Connects generative AI, language models, chatbots, web search, prompt engineering, APIs, data analysis, log analysis, and systems administration.

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This explainer was generated by Manning's knowledge graph from the text of AI for Everyday IT. The section links point to the parts of the book it draws on.