Prompt
An input, typically containing instructions or context, supplied to an artificial-intelligence model to guide or elicit its output.
Software Testing with Generative AI
A prompt is an input instruction or context supplied to a language model to guide its generated output.
- How it is used
- Uses targeted instructions to obtain useful assistance for testing, development, test planning, automation, and test-data creation.
- How deep it goes
- Defines prompts and develops construction tactics through worked examples, but stops short of guaranteeing reliable or self-validating outputs.
- Examples
- Demonstrates file-upload tests, contact-form risks, code-risk analysis, booking-list test cases, Java comments, unit checks, and JSON datasets.
- What you can do after
- Apply it via prompt-engineering tactics to request focused suggestions, structured outputs, test cases, risks, code, comments, or test data.
Read how Software Testing with Generative AI explains prompt →
Coding with AI
A prompt is natural-language input that instructs a generative artificial-intelligence system what response or result to produce.
- How it is used
- Guides generative AI tools during planning, coding, review, debugging, documentation, and UI design.
- How deep it goes
- Explains prompt components, prompt types, contextual refinement, and iterative validation without detailing model internals.
- Examples
- Uses TODO-stack planning, discount-function debugging, SQLite connection comments, practice-test design, and Mermaid workflow generation.
- What you can do after
- Apply it via chat and coding tools to request targeted designs, code, analysis, and revisions while validating results.
AI for Everyday IT
A prompt is an instruction or input supplied to an AI system to elicit a specified response or output.
- How it is used
- Uses prompts to guide AI for writing, workplace communication, learning, documentation, coding, and problem-solving.
- How deep it goes
- Defines prompts, demonstrates zero-shot and specialized forms, and develops refinement through examples, testing, and iteration.
- Examples
- Demonstrates project management questions, ESL practice, email drafting, Pester migration, career planning, and AI citation.
- What you can do after
- Apply prompts via ChatGPT, Claude, Gemini, or Copilot, then evaluate and refine their outputs for the task.
WordPress Power Toolkit: Harness AI to build next-level websites
A prompt is an instruction or question supplied to an AI system to elicit a response or generated content.
- How it is used
- Guides AI to brainstorm, write, organize websites, critique designs, and review audience messages.
- How deep it goes
- Explains prompt construction through role, job, context, format, examples, revisions, and AI-assisted expansion without technical implementation details.
- Examples
- Uses bakery profiles, bass-fishing site titles and menus, decluttering advice, image generation, and blog-comment approval.
- What you can do after
- Apply it via GPT, Claude, Copilot, Midjourney, or Canva to produce and refine targeted responses or design ideas.
Read how WordPress Power Toolkit: Harness AI to build next-level websites explains prompt →
Build a Website with ChatGPT
A prompt is a natural-language instruction or request supplied to an artificial-intelligence model to guide its response.
- How it is used
- Guides ChatGPT to generate web-page code, design suggestions, writing ideas, and revisions.
- How deep it goes
- Explains prompt construction, generated HTML/CSS handling, testing, and iterative revision without model internals.
- Examples
- Uses Hello World!, a book club page, an online journal, an information site, and bread-baking writing ideas.
- What you can do after
- Apply it via ChatGPT to request, refine, test, and save code for simple web pages.
Grokking AI Applications
A prompt is an instruction or input provided to a generative AI model to elicit a response or guide content generation.
- How it is used
- Guides LLM generation and provides the foundation for GenAI applications.
- How deep it goes
- Defines prompts, explains context and length trade-offs, and supplies templates and enhancement techniques without pursuing model internals.
- Examples
- Uses stateless name recall, Italian capital completion, structured templates, and the Healthy Chef workflow as teaching examples.
- What you can do after
- Apply it via prompt engineering to design, test, refine, and connect structured prompts in GenAI workflows.
Generative AI in Action
A system prompt is an instruction supplied to a generative model that establishes behavior, information, or constraints for its responses.
- How it is used
- Sets a chat assistant’s behavior, rules, and task context before user messages, including in nonchat uses of chat completion.
- How deep it goes
- Shows message-array placement, API examples, grounding, format refinement, and token management; does not derive model internals.
- Examples
- Uses a rhyming assistant, a grounded technical-support chatbot, pet-only chat, and JSON entity extraction.
- What you can do after
- Build it from scratch by placing explicit behavior and format instructions in a system message, then testing and refining responses.
Introduction to Generative AI, Second Edition
A prompt is the text or other input supplied to a generative model to specify or guide the output it should produce.
- How it is used
- Guides generative models toward requested answers, task formats, or images through supplied input.
- How deep it goes
- Defines prompts and explains instructions, context, examples, and chat components without implementation-level detail.
- Examples
- Uses article-topic sorting, executive-summary wording, confidential contract revision, and malicious instructions in external content.
- What you can do after
- Practice/perform clearer prompting with instructions and examples while recognizing privacy and injection risks.
Read how Introduction to Generative AI, Second Edition explains prompt →
AI Agents and Applications
A system prompt is an instruction message supplied to a language model to establish high-level behavior or constraints.
- How it is used
- Guides an LLM’s high-level behavior in API requests, tool-using agents, and agent routing.
- How deep it goes
- Shows message placement and worked agent instructions, then traces tool selection; does not establish that instructions make agents invariably reliable.
- Examples
- Uses a phishing reminder, a retrieved-context Q&A, beach-town and weather tools, and a specialist-agent router.
- What you can do after
- Build it from scratch as a system-role message, then apply it in LangGraph to guide tool use or route requests.
Prompt Engineering in Practice
A prompt is an instruction and context artifact supplied to a model to specify a task, constraints, and expected output.
- How it is used
- Specifies model tasks, constraints, inputs, and output formats for software workflows.
- How deep it goes
- Works through a componentized incident-summary prompt and a management lifecycle; defers deployment pipelines, telemetry backends, and multimodel routing.
- Examples
- Uses an incident-report template and an HR policy chatbot’s conflicting instructions to show design and failure.
- What you can do after
- Practice/perform it: draft, test, review, deploy, and retire a prompt through a structured lifecycle.
Build AI-Enhanced Web Apps
A basic text prompt is a single string containing instructions or questions for an AI model.
- How it is used
- Supplies instructions or questions to a model for single-turn generation in a web application.
- How deep it goes
- Defines the string form, shows its `generateText` equivalent as one user message, and contrasts it with conversation-capable messages.
- Examples
- Shows a greeting passed to `generateText` and a programming-language request made more precise by specifying five results.
- What you can do after
- Apply it via the Vercel AI SDK’s `generateText` for a one-off request, then switch to messages when history is needed.
Build a Text-to-Image Generator (from Scratch)
A text prompt is a natural-language instruction or description that specifies desired generated content.
- How it is used
- Guides image retrieval or conditions text-to-image generation from a natural-language description.
- How deep it goes
- Traces tokenization, padding, embeddings, and downstream generation with implementation examples; uses pretrained components rather than training them from scratch.
- Examples
- Uses a beach-playing dog for retrieval, a lion and tiger for encoding, and an astronaut riding a unicorn for generation.
- What you can do after
- Apply it via StableDiffusionPipeline to generate images and inspect how CLIP tokenizes and embeds the input.
Read how Build a Text-to-Image Generator (from Scratch) explains prompt →
Effective Conversational AI
A prompt is an input supplied to a language model that provides a task, instructions, context, or other conditioning information.
- How it is used
- Guides language-model responses in conversational assistants and supports designing, improving, and testing dialogue flows.
- How deep it goes
- Works through prompt assembly, iterative instructions, simulated conversations, and extraction examples, while showing where prompting alone fails.
- Examples
- Uses a recipe request, an empathetic airline greeting, insurance-claim dialogues, simulated users, and transcript-to-JSON extraction.
- What you can do after
- Practice/perform prompt iteration for conversational tasks, then test outputs against user effort, required fields, and failure cases.
Data Storytelling with Altair and AI
A prompt is an input text that provides instructions to a generative AI system about the content or operation to produce.
- How it is used
- Guides generative AI to create textual context, images, code suggestions, and possible next steps for data stories.
- How deep it goes
- Defines prompts and develops reusable structures for ChatGPT and DALL-E, while deferring detailed prompt-engineering guidance.
- Examples
- Uses fragility-title variants, sports chart titles, generated icons, campaign sharing options, and population-policy ordering.
- What you can do after
- Apply it via ChatGPT, DALL-E, and Copilot by specifying output goals, refining results, and checking generated proposals.
Read how Data Storytelling with Altair and AI explains prompt →
Build Python Web Apps with Streamlit
A prompt is an input supplied to a language model, typically consisting of one or more conversational messages.
- How it is used
- Supplies instructions and conversation context for LLM features, from trivia questions to customer-support responses.
- How deep it goes
- Defines prompts as role-and-content messages and shows their construction, API submission, revision, and placement in a graph.
- Examples
- Uses a ten-word Streamlit explanation, a multi-question trivia game, and an on-topic customer-support chatbot.
- What you can do after
- Apply it via the OpenAI Python client or LangGraph to send messages and set task-specific context for an LLM.
Read how Build Python Web Apps with Streamlit explains prompt →
The Complete Obsolete Guide to Generative AI
A prompt is an input instruction or context supplied to a generative artificial-intelligence model to elicit an output.
- How it is used
- Guides generative-model outputs for chat, document queries, media creation, API calls, and automation.
- How deep it goes
- Defines prompts through concrete refinement strategies, contextual injection, and configuration, while deferring broader API capabilities to reference documentation.
- Examples
- Demonstrates constrained science and email requests, customer-support wording, document-grounded Sherlock Holmes queries, contracts, and image descriptions.
- What you can do after
- Apply it via interactive tools or APIs to request focused outputs, refine failed results, and constrain format and context.
Read how The Complete Obsolete Guide to Generative AI explains prompt →
Starting Data Analytics with Generative AI and Python
An input instruction or contextual payload that directs a generative AI system to produce a response.
- How it is used
- Directs generative AI toward analytics tasks such as review keyword generation and sentiment classification.
- How deep it goes
- Develops practical wording, context, output-format, iteration, and validation guidance, without descending into model architecture.
- Examples
- Uses a Portuguese-review keyword request, a Python ChatGPT API call, and sentiment questions checked against review scores.
- What you can do after
- Apply it via a ChatGPT API call or direct sentiment request, then inspect formatting and validate results before use.
Read how Starting Data Analytics with Generative AI and Python explains prompt →
Data Analysis with LLMs
A textual instruction supplied to a language model that specifies a task, context, inputs, and often the desired output format.
- How it is used
- Directs a language model to classify data or translate questions into queries by supplying the task, inputs, and relevant context.
- How deep it goes
- Defines prompt components and works through reusable templates and multimodal inputs, without descending into model internals.
- Examples
- Uses labeled laptop reviews, a movie-review sentiment question, a database schema and question, and image-based questions.
- What you can do after
- Apply it via OpenAI’s Python library to generate prompts from templates, submit them, and collect classifications.
Machine Learning Platform Engineering
A system prompt is an instruction supplied to a language model to establish its role, behavioral constraints, and response requirements.
- How it is used
- Directs a documentation assistant’s role, scope, answer format, and use of retrieved context.
- How deep it goes
- Develops five prompt functions, contrasts a vague baseline with explicit rules, and shows managed-template integration.
- Examples
- Contrasts a generic documentation-assistant instruction with a scoped prompt and creates a templated Langfuse chat prompt.
- What you can do after
- Apply it via Langfuse to version, fetch, compile, and track a documentation assistant’s system prompt.
Read how Machine Learning Platform Engineering explains prompt →
Natural Language Processing in Action, Second Edition
A prompt is an input sequence of instructions or tokens supplied to a language model to guide its generated output.
- How it is used
- Guides generative chatbot replies, including responses to user messages outside a predefined set.
- How deep it goes
- Defines prompts as generation input and works through templates, variables, calls, and history; does not show prompts reliably controlling output.
- Examples
- Uses Python tutor templates, chatbot exchanges, parent onboarding, and a mistaken number-sequence evaluation.
- What you can do after
- Apply it via LangChain to template chatbot input and supply conversation history, while checking generated replies.
Read how Natural Language Processing in Action, Second Edition explains prompt →
Spring AI in Action
A prompt is input supplied to a language model to elicit a generated response, potentially including text and image context.
- How it is used
- Uses prompts to connect Spring applications with language models for questions, generation, summarization, classification, moderation, translation, and sentiment analysis.
- How deep it goes
- Explains prompt construction, provider abstraction, message roles, templates, and REST integration, while deferring broader conversations and tool interactions.
- Examples
- Demonstrates question answering, board-game rules, role-separated messages, sentiment scoring, document summarization, translation, and image-generation descriptions.
- What you can do after
- Apply it via Spring AI ChatClient to submit user input, add system instructions, and expose generated results through REST services.
The Art of AI Product Development
A prompt is an instruction or contextual input supplied to a generative model to elicit an output.
- How it is used
- Directs content generation and editing, including responses grounded in retrieved documents and boundaries for off-topic requests.
- How deep it goes
- Explains prompt components, reusable templates, and retrieved-context trade-offs without specifying an API format or exact prompt syntax.
- Examples
- Uses AI-compliance content context, retrieved document chunks, and a content editor’s polite refusal of unrelated requests.
- What you can do after
- Practice/perform prompt design by reusing task structures, adding relevant context, and setting boundaries for unrelated requests.
Read how The Art of AI Product Development explains prompt →
Build an AI Agent (From Scratch)
An instruction supplied to a language model that establishes high-level behavior or constraints for an interaction.
- How it is used
- Sets persistent behavioral rules for an agent’s responses and multi-step decisions.
- How deep it goes
- Defines prompt categories, details system-prompt roles and actionable guidelines, and examines the cost of packing multiple roles into one prompt.
- Examples
- Uses weather searches, clarification rules, and a technical-blog assistant combining research, coding, writing, and review.
- What you can do after
- Build it from scratch by writing agent instructions for action, clarification, and tool selection, then splitting crowded roles when needed.
The Quick Python Book, Fourth Edition
A prompt is an instruction or input supplied to an AI system to elicit a particular response or generated output.
- How it is used
- Uses prompts to request Python code for calculations, file processing and changes to existing programs.
- How deep it goes
- Develops a practical checklist of requirements, context and constraints, then tests generated results; does not explain generator internals.
- Examples
- Shows requests for temperature statistics, text cleaning, word counts, wc-style options and weather-data extraction.
- What you can do after
- Practice/perform prompt writing for AI code generators, then run and evaluate the generated Python code.
Read how The Quick Python Book, Fourth Edition explains prompt →
Deep Learning with Python, Third Edition
A short text description used to condition image generation, including both positive and negative forms in this section.
- How it is used
- Guides text-to-image diffusion by describing the desired image and, optionally, what to avoid.
- How deep it goes
- Explains prompt encoding and denoising, then separates generation into embedding, denoising, and output-scaling steps without training a text-to-image model from scratch.
- Examples
- Uses an astronaut-on-an-origami-elephant image, a negative prompt for blue, and an interpolation between dog and creature descriptions.
- What you can do after
- Apply it via KerasHub’s TextToImage task to generate images, steer away from an attribute, or interpolate between prompt embeddings.
Read how Deep Learning with Python, Third Edition explains prompt →
Building LLM Applications with DSPy
Textual instructions or input supplied to a language model to elicit a response.
- How it is used
- Provides the LM with task instructions and input, whether written directly or generated from a DSPy signature and module.
- How deep it goes
- Explains editable prompt sections and shows custom instruction changes, without prescribing universally optimal wording.
- Examples
- Compares a hardcoded intent-classification prompt with a DSPy-generated prompt and appends a closing instruction in a custom module.
- What you can do after
- Apply it via DSPy signatures, modules, inspection, and optimizers to generate and compare prompts without handcrafting each candidate.
Read how Building LLM Applications with DSPy explains prompt →
LLMs in Production
A persistent prompt instruction applied to requests to establish consistent model behavior or interaction style.
- How it is used
- Establishes reusable behavior for an assistant while user input and retrieved information vary between tasks.
- How deep it goes
- Defines the system component and works through an assembled chatbot prompt, without specifying a guaranteed way to enforce its instructions.
- Examples
- Uses a company-policy chatbot answering a 401(k) question and a Python coding prompt assembled with retrieved examples.
- What you can do after
- Practice/perform it by drafting reusable behavior instructions and testing their wording alongside user input and context.
Deep Learning with R, Third Edition
A prompt is an input specification that identifies the object or region for which an image-segmentation model should produce a mask.
- How it is used
- Identifies a segmentation target or supplies direction for image and text generation.
- How deep it goes
- Explains point and box inputs, model components, and training triples, but defers prompt-encoder and mask-decoder internals.
- Examples
- Uses point and box segmentation prompts and a Stable Diffusion 3 image generated from an astronaut description.
- What you can do after
- Apply it via KerasHub TextToImage generation and understand how point or box prompts identify segmentation targets.
Read how Deep Learning with R, Third Edition explains prompt →
A Pythonic Adventure: From Python basics to a working web app
A prompt is an instruction or question supplied to an AI system to elicit a response.
- How it is used
- Uses precise role-based requests to obtain code reviews, explanations, corrections, and ideas from AI.
- How deep it goes
- Explains prompt precision through role, task, code, and follow-up examples, without covering prompt internals.
- Examples
- Uses a read_menu function, list-comprehension questions, a TypeError traceback, and analogy questions as prompt examples.
- What you can do after
- Apply it via ChatGPT to request targeted programming help and continue with clarifying questions.
Read how A Pythonic Adventure: From Python basics to a working web app explains prompt →
Learn Generative AI with PyTorch
The chat message role that sets assistant behavior in the OpenAI chat-completions format.
- How it is used
- Sets assistant behavior in OpenAI chat-completions requests.
- How deep it goes
- Defines role and effect, then demonstrates several behavioral instructions without detailing internal model processing.
- Examples
- Uses factual answering, essay writing, joke telling, and follow-up conversation as concrete API examples.
- What you can do after
- Operate/configure an OpenAI chat completion with a behavior-setting system message and a user request.
AI Agents in Action
A system prompt is a high-priority instruction supplied to a language model to establish its behavior, role, and response constraints.
- How it is used
- Establishes agent behavior, roles, and response constraints in personas, iterative prompting, and question answering.
- How deep it goes
- Explains system prompts through persona configuration, iterative chat, and a templated question-and-answer prompt.
- Examples
- Uses an Ogre-speaking persona, a time-travel movie recommendation, and a contextual question-answering Jinja2 template.
- What you can do after
- Apply it via YAML profiles or prompt templates to shape agent roles and constrain answers against supplied context.
Effective Data Analysis
A prompt is user-provided input that instructs a generative model about the content or behavior expected in its output.
- How it is used
- Directs generative AI toward defined analytics tasks and stakeholder-relevant outputs.
- How deep it goes
- Defines prompts operationally and demonstrates refinement, but does not explain model internals or training.
- Examples
- Uses login statistics, rats_weather.csv, predictive models, Python code, and city-agency recommendations as prompt-driven examples.
- What you can do after
- Apply it via ChatGPT or another chatbot to request analyses, code, models, metrics, and constrained recommendations.
AI Agents in Action, Second Edition
A prompt is an instruction or contextual input supplied to a language model to guide the content and behavior of its response.
- How it is used
- Uses prompts to make language-model agents predictable, productive, cost-aware, and reliable.
- How deep it goes
- Explains prompt structure, caching, failure modes, and SDK boundaries, but stops short of model internals.
- Examples
- Uses a research-agent prompt, persona and delimiter patterns, caching placement, and a retrieval-agent output-format correction.
- What you can do after
- Build it from scratch by writing structured prompts, decomposing overloaded tasks, and adapting instructions around SDK-provided metadata.
Read how AI Agents in Action, Second Edition explains prompt →
Microsoft Azure in Action
A prompt is text or other input instructions supplied to a generative model to guide its output.
- How it is used
- Guides Azure OpenAI text generation for tasks such as recommendations, summaries, and answers.
- How deep it goes
- Shows a worked prompt and its request field, but does not develop a general prompt-writing procedure.
- Examples
- Uses a customer review of *1984* to request three book recommendations with brief descriptions.
- What you can do after
- Apply it via an Azure OpenAI REST request to ask a deployed model for tailored recommendations.
AI-Powered Developer: Build software with ChatGPT and Copilot
A natural language prompt is an instruction or query expressed in ordinary human language for directing an AI system.
- How it is used
- Directs ChatGPT, Copilot, and CodeWhisperer toward code, explanations, debugging, refactoring, or architectural assistance.
- How deep it goes
- Explains prompts as model inputs and develops context-sensitive refinement through coding examples, without detailing model internals.
- Examples
- Uses linked-list reversal, kth-smallest selection, Flask setup, dashboard creation, memory-leak analysis, and refactoring requests.
- What you can do after
- Apply it via ChatGPT, GitHub Copilot, or CodeWhisperer to request code and refine requirements toward a desired result.
Read how AI-Powered Developer: Build software with ChatGPT and Copilot explains prompt →
Sutskever's List
A prompt is an input containing instructions, examples, or context provided to a model to elicit a response.
- How it is used
- Uses prompts to reveal existing model capabilities and support more reliable multistep reasoning.
- How deep it goes
- Moves from puzzle failures to explicit state representations and chain-of-thought prompting.
- Examples
- Uses a changed-rules river-crossing puzzle, state triples, and stepwise math or logic problems.
- What you can do after
- Apply explicit state tracking or chain-of-thought prompts to improve multistep reasoning without additional training.
Time Series Forecasting Using Foundation Models
A system prompt is an instruction supplied to a language model that establishes its context, behavior, tone, or response constraints.
- How it is used
- Frames forecasting, anomaly detection, and contextual time-series modeling as language-model tasks.
- How deep it goes
- Explains prompt roles, formatting, API delivery, and output parsing, but defers robust safeguards and model internals.
- Examples
- Uses comma-separated sequences, forecast requests, anomaly labels, weekly sales context, and daily website-visit seasonality.
- What you can do after
- Apply it via API calls or Time-LLM configuration to provide context, request forecasts, and constrain model output.
Read how Time Series Forecasting Using Foundation Models explains prompt →
Regular Expression Puzzles and AI Coding Assistants
Prompt phrasing is the particular wording and structure used to formulate an instruction for an AI system.
- How it is used
- Frames requests to AI coding assistants while evaluating generated regular expressions and functions.
- How deep it goes
- Examines wording changes through concrete outputs and counterexamples, while deferring any general method for finding optimal prompts.
- Examples
- Uses IPv4 matching, domino-pattern matching, poker-hand pair detection, and function naming outputs.
- What you can do after
- Evaluate/understand how alternate requests and surrounding context can change AI-generated code without guaranteeing correctness.
Read how Regular Expression Puzzles and AI Coding Assistants explains prompt →
Deep Learning with PyTorch, Second Edition
User-provided guidance such as points, boxes, or masks that steers the segmentation model toward a desired object or region.
- How it is used
- Guides image generation, inpainting, and segmentation toward specified content or regions.
- How deep it goes
- Distinguishes prompt roles and demonstrates invocation, while deferring model internals and prompt-generation details.
- Examples
- Uses a horse-to-zebra edit and segmentation inputs consisting of points, boxes, or masks.
- What you can do after
- Apply prompts through an inpainting pipeline or use them to guide segmentation masks without task-specific fine-tuning.
Read how Deep Learning with PyTorch, Second Edition explains prompt →
Reinforcement Learning from Human Feedback
A prompt is an input text or instruction provided to a language model to elicit a response.
- How it is used
- Structures model context for interaction, training, evaluation, RLHF rollouts, chat formatting, and tool use.
- How deep it goes
- Explains prompt effects, role formatting, RLHF structure, tool calls, and evaluation examples without deriving prompting algorithms.
- Examples
- Uses pirate-style system instructions, helicopter questions, multiple-choice reasoning prompts, tool schemas, and Fibonacci code execution.
- What you can do after
- Apply it via chat templates, RLHF datasets, evaluation prompts, and tool-orchestration loops.
Read how Reinforcement Learning from Human Feedback explains prompt →
Building Reliable AI Systems
Input instructions or context supplied to an AI model to guide its response.
- How it is used
- Guides LLM responses in chatbots and production assistants, where vague instructions can cause inconsistent behavior.
- How deep it goes
- Explains prompt components through contrasting examples and extends them to production expectations and version selection, without detailing model internals.
- Examples
- Contrasts two ocean prompts, describes a legal assistant’s contradictory advice, and constructs context-bound health-care policy answers.
- What you can do after
- Build it from scratch by specifying instructions, context, input, and response expectations; version prompts when consistent production behavior matters.
How Large Language Models Work
A prompt is an input text or other input provided to a language model to elicit an output.
- How it is used
- Constrains language-model behavior toward a particular task, format, tone, and set of goals.
- How deep it goes
- Defines prompting and explains framing sensitivity, but defers detailed prompt construction and model internals.
- Examples
- Uses CSV conversion, survey-theme categorization, car-salesperson roleplay, and stylized writing as examples.
- What you can do after
- Apply it by framing bounded inputs for data processing, categorization, or stylized generation while limiting unpredictable requests.
Get Programming: Learn to code with Python
A prompt is text displayed by a program to request information from a user.
- How it is used
- Uses prompts to begin interactive programs and collect information that guides later operations.
- How deep it goes
- Explains display, waiting, spacing, storage, string handling, and numeric conversion through short code examples.
- Examples
- Uses name, location, secret, color, symbol, and number questions, including stored responses and calculations.
- What you can do after
- Apply it via Python's input() function to request, store, transform, and calculate with user responses.
Read how Get Programming: Learn to code with Python explains prompt →
Digital Twins in Action
A system prompt is a high-level instruction supplied to a model that establishes an agent's role, behavior, and constraints.
- How it is used
- Directs a climate-control agent’s decisions during a multi-step reasoning and tool-use loop.
- How deep it goes
- Shows a concrete prompt with constants and decision steps, but does not explain how the model interprets those instructions internally.
- Examples
- Uses a home climate-control policy with temperature thresholds, baseload estimates, excess-solar calculations, and AC hysteresis.
- What you can do after
- Operate/configure it to set an agent’s role, priorities, assumptions, and control steps.
Snowflake Data Engineering
A prompt is a set of questions or instructions and supporting context supplied to a generative model to guide its output.
- How it is used
- Guides an LLM through extracting structured records from unstructured email within a Snowflake data pipeline.
- How deep it goes
- Defines prompt engineering and demonstrates iterative, schema-constrained construction, but does not explain LLM internals.
- Examples
- Uses bakery delivery emails, constrained bakery items, five CSV columns, and YYYY-MM-DD date formatting.
- What you can do after
- Build it from scratch by writing prompts that produce structured LLM output for downstream transformation and storage.
AI-Powered Search
A prompt is input text supplied to a language model to provide instructions, questions, or contextual material.
- How it is used
- Constructs context and instructions for foundation models that summarize retrieved search results.
- How deep it goes
- Explains contextual token interpretation and prompt assembly, but defers Transformer implementation details.
- Examples
- Uses retrieved web results, URLs, citation markers, and a “Be concise” instruction to demonstrate summarization.
- What you can do after
- Apply it via a search pipeline by combining a query, retrieved results, citations, and output instructions.
Modern Angular
A prompt is text supplied to an AI system as an instruction or question that guides its generated response.
- How it is used
- Uses ChatGPT to accelerate Angular unit-test preparation, especially mock data, service dependencies, and component tests.
- How deep it goes
- Explains prompt specificity through technology references and constraints, but stops short of a general prompt-engineering framework.
- Examples
- Demonstrates typed TimeOffRequest arrays, TimeOffRequestService mocks, and generated ConfirmationDialogComponent tests.
- What you can do after
- Apply it via ChatGPT to generate test fixtures, dependency mocks, and candidate Angular unit tests.
Grokking AI Algorithms, Second Edition
A prompt is an instruction, question, or other text input provided to a language model to elicit a response.
- How it is used
- Uses text directions to guide language and image models toward useful generated content.
- How deep it goes
- Explains prompts as model guidance and shows their role in generation, but does not develop prompt-engineering techniques.
- Examples
- Uses scene descriptions, article outlines, slogans, reports, product descriptions, practice questions, and lesson summaries.
- What you can do after
- Apply it via a language or image model to create a first draft or generate visual content for human refinement.
Read how Grokking AI Algorithms, Second Edition explains prompt →
Financial AI in Practice
A prompt is an instruction or input supplied to a generative model to guide its output.
- How it is used
- Guides LLM output for financial products, including personalized investment summaries and portfolio allocation recommendations.
- How deep it goes
- Shows prompt formatting and API calls, while leaving complex production RAG infrastructure beyond the illustrative examples.
- Examples
- Uses investment-post summaries and prompts combining macroeconomic data, ETF signals, and current portfolio weights.
- What you can do after
- Build it from scratch by formatting financial inputs into a prompt and passing it to an LLM API.
Build Applications with Local AI Models on a Mac
An input instruction or request submitted to an AI model for processing.
- How it is used
- Provides the input an AI model processes to produce a response.
- How deep it goes
- Defines prompts and names input forms without explaining how to design them.
- Examples
- Names questions, instructions, voice transcripts, and blocks of text as prompt forms.
- What you can do after
- Apply prompts through Python’s Ollama API to send input to a local model and receive responses.
Read how Build Applications with Local AI Models on a Mac explains prompt →
Related concepts
- prompt engineeringused by · Prompt engineering is the deliberate design and refinement of instructions, context, examples, or other inputs to guide an AI model’s behavior or outputs.
- language modeluses · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.
- contexthas part · Context is the surrounding information, state, or circumstances that condition how an entity, event, or task is interpreted or how a system behaves.
- ChatGPTuses · ChatGPT is OpenAI’s conversational AI service, which lets users interact with generative AI models and related tools through natural-language prompts.
- ai coding assistantused by · An AI coding assistant is an artificial-intelligence-powered software tool or agent that helps users perform software-development tasks such as generating, modifying, explaining, testing, or debugging code.
- prompt componenthas part · A prompt component is a distinct part of a prompt that provides information, instructions, examples, or other content for a particular function.
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