ChatGPT
ChatGPT is OpenAI’s conversational AI service, which lets users interact with generative AI models and related tools through natural-language prompts.
Starting Data Analytics with Generative AI and Python
A conversational generative-AI system developed by OpenAI that responds to user prompts with generated content.
- How it is used
- Supports conversational and programmatic data analysis, including review classification, interpretation, summarization, and coding assistance.
- How deep it goes
- Works through prompts, Python API calls, output checks, and measured results rather than model architecture.
- Examples
- Uses Portuguese customer reviews, a 500-review sentiment test, and regional delivery-time differences as analytical cases.
- What you can do after
- Apply it via the web interface or OpenAI API to propose analytical steps, then validate code and findings against data.
Read how Starting Data Analytics with Generative AI and Python explains ChatGPT →
AI for Everyday IT
ChatGPT is OpenAI's conversational generative AI service for interacting with users through natural-language prompts and responses.
- How it is used
- Supports IT research, troubleshooting, documentation, scripting, collaboration, and routine workplace tasks through conversational prompts.
- 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 you can do after
- Apply ChatGPT to research, analyze files, draft work products, troubleshoot systems, and automate repetitive IT tasks while validating results.
Build a Website with ChatGPT
ChatGPT is a conversational artificial-intelligence system that generates or transforms text and code in response to natural-language prompts.
- How it is used
- Generates web-page code and assists with site writing from plain-English prompts.
- How deep it goes
- Defines GPT’s name and behavior, then gives prompt structure, a workflow, testing, and revision without implementation internals.
- Examples
- Demonstrates a Hello World display, a random-pun button page, title suggestions, and online-journal and photo-gallery prompts.
- What you can do after
- Apply it via ChatGPT or Copilot to prompt, test, refine, save, and deploy generated web-page code.
Data Storytelling with Altair and AI
ChatGPT is a generative AI conversational system that produces and transforms text from user prompts.
- How it is used
- Generates audience-tailored textual context, calls to action, and candidate next steps for data stories.
- How deep it goes
- Explains prompt structures, iterative refinement, and practical story cases, while deferring generative-AI fundamentals.
- Examples
- Uses homelessness subtitles, sports-investment titles, pet-campaign sharing options, hotel feasibility options, and population-policy sequencing.
- What you can do after
- Apply it through ChatGPT to draft and refine audience-specific chart text and investigate proposed next steps.
Read how Data Storytelling with Altair and AI explains ChatGPT →
Coding with AI
ChatGPT is a conversational generative-artificial-intelligence service that generates text and code in response to user prompts.
- How it is used
- Accelerates software design, requirements analysis, coding support, testing, debugging, documentation, and UI drafting through conversational prompts.
- How deep it goes
- Explains prompt-driven workflows through design, requirements, user stories, diagrams, and HTML examples, while deferring detailed testing and debugging practice.
- Examples
- Demonstrates a certification practice-test web app with MariaDB, Flask, Vue, extracted requirements, user stories, a Mermaid flowchart, and shared HTML.
- What you can do after
- Apply it via a conversational interface to produce and refine software planning, code, tests, diagrams, and documentation.
Introduction to Generative AI, Second Edition
ChatGPT is a conversational language-model system from OpenAI that generates responses to user prompts.
- How it is used
- Serves as a conversational tool for drafting, explaining, summarizing, and answering questions.
- How deep it goes
- Defines the web-based chatbot through its uses and consequences, without explaining its implementation.
- Examples
- Uses poetry and code requests, fabricated legal citations, a confidential contract, and a patient-facing script.
- What you can do after
- Evaluate/understand it as a drafting aid whose factual claims and data-sharing risks require scrutiny.
Read how Introduction to Generative AI, Second Edition explains ChatGPT →
Data Analysis with LLMs
ChatGPT is a conversational artificial-intelligence service that lets users interact with OpenAI language models through natural-language messages.
- How it is used
- Supports data analysis either by processing supplied text directly or by turning natural-language requests into code for other tools.
- How deep it goes
- Walks through account access, conversation controls, text tasks, and a CSV upload; stops short of language-model internals.
- Examples
- Uses product reviews for text analysis and a review table for counting records by product category.
- What you can do after
- Apply it via the ChatGPT web interface to ask analysis questions, upload a CSV, and inspect generated code before relying on results.
Inside AI
ChatGPT is a conversational artificial-intelligence system that generates responses to natural-language prompts.
- How it is used
- Illustrates accessible conversational AI for content creation and other natural-language applications.
- How deep it goes
- Explains capabilities through exchanges and failure cases, but stops short of model internals.
- Examples
- Uses repeated biographical questions and fabricated legal citations to show how plausible answers can be wrong.
- What you can do after
- Evaluate/understand it as a drafting aid whose factual answers require independent checking.
Build Financial Software with Generative AI (From Scratch)
A conversational generative-AI system that responds to natural-language prompts and can generate text or code.
- How it is used
- Supports prompting for regulation summaries, Python code, code revisions, and debugging during financial-software development.
- How deep it goes
- Demonstrates iterative prompting, generated-code inspection, debugging, and version verification without comprehensively teaching generative AI.
- Examples
- Uses Check 21 summaries, a current-balance formatter, a transaction-list defect, and ACH parser outputs from ChatGPT 3.5 and 4.
- What you can do after
- Evaluate/understand generated responses by refining prompts, testing code, checking domain rules, and validating version-sensitive advice.
Read how Build Financial Software with Generative AI (From Scratch) explains ChatGPT →
Software Testing with Generative AI
ChatGPT is a conversational artificial-intelligence system that generates responses to user prompts using language-model technology.
- How it is used
- Applies prompt-driven language generation to testing, development, UI automation, and exploratory understanding.
- How deep it goes
- Explains prompt use through worked code and testing examples, but stops short of treating generated output as reliable without verification.
- Examples
- Uses conversational prompts, Java API-client generation, Selenium page objects, contact-form checks, code comments, and visual process models.
- What you can do after
- Apply it via focused prompts to draft code, improve automated checks, and clarify unfamiliar systems while independently validating results.
Read how Software Testing with Generative AI explains ChatGPT →
Grokking AI Applications
ChatGPT is a conversational AI system built on generative language models and trained to respond to human users.
- How it is used
- Uses generative language modeling to support meaningful conversations with human users.
- How deep it goes
- Explains three training stages and application-level extensions, but stops short of detailed model internals.
- Examples
- Uses GPT sentence completion, human-guided refinement, reinforcement learning, and chatbot conversations as teaching examples.
- What you can do after
- Evaluate ChatGPT as a layered system rather than as an isolated language model.
LLMs in Production
ChatGPT is a conversational AI service that provides an interactive interface for generating responses from large language models.
- How it is used
- Positions ChatGPT as an accessible interface for turning large language models into a widely usable product.
- How deep it goes
- Explains its product-level significance and reliability challenge, but stops short of describing model architecture or training internals.
- Examples
- Uses difficult user questions, rapid adoption, and the contrast between a one-time demo and a service serving millions.
- What you can do after
- Evaluate generative-AI products by accessibility, reliability at scale, and the gap between prototypes and dependable services.
The Complete Obsolete Guide to Generative AI
A version of the ChatGPT conversational artificial-intelligence service released in 2022.
- How it is used
- Uses ChatGPT to clarify specific programming problems, draft workplace writing, and generate task-focused starting points.
- How deep it goes
- Explains prompt specificity through concrete questions and generated code, while deferring broad instruction to tutorials and hands-on practice.
- Examples
- Uses Python list iteration, 4K-to-1080p video conversion, an after-action report, and an essay with fabricated references.
- What you can do after
- Apply it via ChatGPT by framing focused prompts, using outputs as drafts, and verifying code, facts, and references.
Read how The Complete Obsolete Guide to Generative AI explains ChatGPT →
Troubleshooting Java, Second Edition
ChatGPT is a conversational AI system that generates natural-language responses to user prompts.
- How it is used
- Uses ChatGPT as an early aid for generating troubleshooting ideas and understanding Java application behavior.
- How deep it goes
- Introduces prompting and investigative value but stops short of detailed prompt design, validation methods, or implementation internals.
- Examples
- Uses an AI prompt describing a problem to identify possible troubleshooting starting points.
- What you can do after
- Apply it via ChatGPT to generate initial investigation ideas and accelerate understanding of application behavior.
Read how Troubleshooting Java, Second Edition explains ChatGPT →
How Large Language Models Work
A conversational artificial-intelligence service developed by OpenAI that generates responses to natural-language prompts.
- How it is used
- Frames ChatGPT as a practical generative-AI system for consumers, developers, and business decision makers.
- How deep it goes
- Defines ChatGPT and its surrounding terms in plain language, while deferring detailed treatment of pretraining and transformers.
- Examples
- Uses a haiku prompt and input-output examples spanning text, images, audio, and numbers.
- What you can do after
- Understand what ChatGPT is, what it can and cannot do, and when related systems may be unsuitable.
Grokking Relational Database Design
ChatGPT is a conversational generative artificial intelligence service that produces responses to user prompts.
- How it is used
- Accelerates database design by supporting requirements, entity design, SQL generation, verification, and visualization.
- How deep it goes
- Explains prompting practices and a complete workflow, but stops short of guaranteeing correct answers or replacing design verification.
- Examples
- Uses a spaceship-manufacturer database, structured prompts, generated MySQL code, and dbdiagram.io diagrams.
- What you can do after
- Build database designs from requirements and communicate them through verified text, SQL, and diagrams.
Read how Grokking Relational Database Design explains ChatGPT →
Elasticsearch in Action, Second Edition
ChatGPT is a conversational artificial-intelligence system that generates human-like text in response to user prompts.
- How it is used
- Supports conversational assistance for writing, planning, summarization, code analysis, data modeling, and technology comparison.
- How deep it goes
- Explains conversational behavior and next-word prediction, but stops short of describing transformer internals or deployment details.
- Examples
- Uses travel itineraries, legal summaries, self-help plans, code analysis, data models, technology comparisons, and complaint letters.
- What you can do after
- Apply it via conversational prompts to generate text and support varied planning, analysis, summarization, and writing tasks.
Read how Elasticsearch in Action, Second Edition explains ChatGPT →
Learn AI-Assisted Python Programming, Second Edition
ChatGPT is a conversational artificial-intelligence system that generates responses to natural-language prompts using large language models.
- How it is used
- Supports questions, writing, programming, and other knowledge-focused tasks through natural-language interaction.
- How deep it goes
- Defines ChatGPT’s broad role and contrasts it with Copilot, but does not explain model internals or implementation.
- Examples
- Uses questions, essays, MBA examinations, Python programs, prompt patterns, and creative persona requests as examples.
- What you can do after
- Apply it to answer questions, write essays, explore creative prompts, and support beginner programming tasks.
Read how Learn AI-Assisted Python Programming, Second Edition explains ChatGPT →
Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI
ChatGPT is a conversational AI system based on large language models that generates responses to user prompts.
- How it is used
- Supports practical tabular-data analysis, from textual feature engineering and visualization to interpretation and reporting.
- How deep it goes
- Demonstrates end-to-end analytical tasks but reports weaker tabular prediction performance than fully supervised models.
- Examples
- Uses CSV files, textual columns, matplotlib commands, regular expressions, charts, tables, and generated analytical reports.
- What you can do after
- Apply it via the Advanced Data Analytics API to analyze CSV data and reproduce results with generated Python code.
Read how Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI explains ChatGPT →
Modern Angular
ChatGPT is an artificial-intelligence conversational system that generates responses from natural-language prompts.
- How it is used
- Uses prompted generation to accelerate mock-data creation, dependency mocking, and Angular unit-test drafting.
- How deep it goes
- Demonstrates concrete prompts and generated TypeScript, but stops short of replacing developer judgment in service mocking.
- Examples
- Uses typed time-off requests, a service mock, generated component tests, and DOM assertions as working examples.
- What you can do after
- Apply it via ChatGPT to generate test fixtures, Observable-based service mocks, and initial Angular component tests.
Related concepts
- conversational aikind of · Conversational AI is artificial intelligence that enables software systems to interact with users through natural-language dialogue.
- language modelkind of · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.
- GPT modeluses · A GPT model is a generative pretrained Transformer language model that produces text autoregressively by predicting successive tokens from preceding context.
- promptused by · An input, typically containing instructions or context, supplied to an artificial-intelligence model to guide or elicit its output.
- GPT-4uses · GPT-4 is a generative large language model developed by OpenAI for understanding and generating text and other supported inputs and outputs.
- reinforcement learning with human feedbackuses · Reinforcement learning with human feedback is a model-training approach that uses human evaluations or preferences to guide optimization of model behavior.
These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.