Generative modeling

As explained in AI for Everyday IT

Generative AI is a class of artificial intelligence systems that produces new content, such as text, images, code, or structured data, from learned patterns and inputs.

Generative artificial intelligence creates new content from patterns learned from existing data. It can produce text, images, code, and structured outputs, helping IT teams complete routine work faster while leaving judgment and context to people. section 1.4 section 1.0

Common IT uses include:

• Extracting and formatting information from PDFs before inserting it into databases. section 1.0
• Analyzing logs and user behavior to identify unusual patterns that may indicate security problems. section 1.0
• Writing documentation, evaluating information, extracting data intelligently, renaming files in batches, and supporting email or messaging workflows. section 1.1 section 7.0
• Creating or editing visual content from descriptions or examples through image generators. section 1.4

Text generators, also called large language models, include OpenAI GPT, Google Gemini, and Anthropic Sonnet. ChatGPT, Claude, Gemini, and Copilot combine generative capabilities with conversational interfaces and, in some cases, business or productivity integrations. section 1.4 section 1.4.1

Drafting disaster-recovery documentation

A MySQL DBA can ask: “I’m a MySQL DBA at an organization with about 100 servers. I’ve been asked to deliver DR documentation and I want you to help me create it. What do you need from me?” The resulting questions request server roles and configurations, backup methods and schedules, storage locations, and recovery objectives such as RTO and RPO. Use enterprise services with appropriate security for company data; with consumer tools, request templates or use placeholders and add real details later. section 14.2.2

Figure 1.1 Image created by DALL-E as described by OpenAI GPT-3.5

Compare GPT-3.5 and GPT-4 responses to a physical stacking problem, illustrating how generated answers can differ in practical reasoning. section 1.1

Generated content is not automatically correct. Verify results, protect sensitive information, and combine AI speed with human expertise, empathy, creativity, and contextual judgment. section 1.0 section 14.2.2

How AI for Everyday IT covers it

How deep it goes

Defines learned-pattern generation and practical uses, but stops short of deep model theory, mathematics, and internal mechanics.

Examples

Uses PDF extraction, log analysis, chatbot comparisons, workplace messaging, application integration, and MySQL disaster-recovery documentation.

What it assumes

Assumes basic IT concepts and teaches foundational AI terminology, use cases, limitations, and practical prompting.

What you can do after

Apply generative AI to routine IT tasks while validating outputs and protecting organizational data.

How it connects

Connects artificial intelligence with language models, image generation, chatbots, APIs, function calling, cybersecurity, and disaster recovery.

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Related concepts

• language modelincludes · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.
• Gaussian mixture modelincludes · A Gaussian mixture model is a probabilistic model representing a distribution as a weighted combination of multiple Gaussian component distributions.
• socio-technical systempart of · A socio-technical system is an interdependent arrangement of people, technology, processes, and social or organizational structures that interact to achieve outcomes.
• artificial intelligencekind of · Artificial intelligence is the field concerned with creating computational systems that perform tasks associated with human intelligence, such as learning, reasoning, perception, or decision-making.
• variational autoencoderincludes · A variational autoencoder is a generative neural network that learns a probabilistic latent representation of data and decodes samples from it to reconstruct or generate data.
• generative adversarial networkincludes · A generative adversarial network is a generative model with a generator and discriminator trained in competition, enabling the generator to produce samples resembling a target dataset or distribution.

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.