User experience
User experience is a person’s overall perceptions and responses resulting from interactions with a product, service, or system.
Design for Developers
The overall quality of a person's interaction with a website or web application, including usability, responsiveness, and emotional impact.
- How it is used
- Frames web planning, implementation, and iteration around users’ needs, tasks, and perceptions.
- How deep it goes
- Defines UX, outlines its facets and roles, and gives an iterative workflow without deep specialist-method detail.
- Examples
- Uses a microwave dial, a scanner waiting queue, project workflows, and customer interviews to illustrate experience decisions.
- What you can do after
- Apply it to plan, test, and refine web experiences while recognizing how technical decisions affect users.
The Art of AI Product Development
User experience is the design of how people access, understand, control, and respond to a product's functionality and outputs.
- How it is used
- Delivers AI-generated value through an interface that guides use, communicates limitations, and preserves appropriate user control.
- How deep it goes
- Develops interface choices and an iterative design process through research, prototype testing, failure simulation, and post-launch feedback; avoids implementation code.
- Examples
- Uses an overpromising financial-data chatbot and a demand forecast with a confidence band to illustrate UX failures and remedies.
- What you can do after
- Practice/perform UX decisions by selecting an interface, testing responses to AI failures, and revising the experience from user feedback.
Read how The Art of AI Product Development explains user experience →
Architecture Modernization: Socio-technical alignment of software, strategy, and structure
User experience is the overall experience a person has while interacting with a product or system, including usability and perceived value.
- How it is used
- Frames UX as a business and architectural modernization concern for both customer-facing products and employee tools.
- How deep it goes
- Explains UX through failure cases and discovery practices, while stopping short of detailed interface-design or implementation instructions.
- Examples
- Uses duplicate rental-car bookings, constrained case entry, inefficient agent tools, and a Citibank payment error to expose UX costs.
- What you can do after
- Practice/perform user-centered discovery to identify UX risks, distinguish interface issues from deeper constraints, and guide modernization priorities.
Effective Platform Engineering
User experience is the overall quality of a user's interaction with a product or service, including its ease, effectiveness, and pleasantness.
- How it is used
- Centers engineering-platform design on the continuous, self-serve experience delivered to development teams.
- How deep it goes
- Explains experience through product principles, lifecycle optimization, user testing, domain boundaries, and platform failure modes.
- Examples
- Uses Kubernetes, Terraform, Git, self-service APIs, alpha-users, and cross-team coordination as experience indicators.
- What you can do after
- Evaluate platform changes through self-service, user feedback, measurable value, observability, and the friction created by team boundaries.
Read how Effective Platform Engineering explains user experience →
Build Python Web Apps with Streamlit
User experience is the overall experience of a person when interacting with a software product, including its usability, behavior, and perceived quality.
- How it is used
- Guides app planning and implementation by keeping user needs, preferences, and usability central to design decisions.
- How deep it goes
- Provides a workflow, sketches, implementation choices, and iteration, but stops short of a formal usability theory.
- Examples
- Uses a unit-converter mock, Streamlit widgets, and removal of a Convert button to demonstrate visible experience improvements.
- What you can do after
- Build it from scratch by sketching, implementing, testing, and refining an app around observed user needs.
Read how Build Python Web Apps with Streamlit explains user experience →
Delivering Machine Learning Projects: From design to deployment
UX design is the design of user interfaces and interactions to support users' needs and the effects of system use.
- How it is used
- Uses UX design during sprint 1 to turn business understanding, user stories, and model requirements into an effective application design.
- How deep it goes
- Explains UX through workflow tasks and validation checkpoints but stops short of detailed interface methods or interaction specifications.
- Examples
- Uses UX designs, application descriptions, user-story backlogs, validation conversations, and ML-project story cards as concrete artifacts.
- What you can do after
- Apply it by creating an application description and user-story backlog, validating them with users, and documenting UX designs.
Read how Delivering Machine Learning Projects: From design to deployment explains user experience →
Build AI-Enhanced Web Apps
User experience is the overall quality of a person's interaction with a product, including its usability, clarity, responsiveness, and satisfaction.
- How it is used
- Guides the design of generative AI interfaces that remain readable, responsive, navigable, accurate, and engaging during model interaction.
- How deep it goes
- Explains formatting, navigation, streaming, validation, and bias auditing, while stopping short of detailed implementation code.
- Examples
- Uses chat message boxes, Markdown rendering, automatic scrolling, collapsible sections, keyword highlighting, streaming responses, and GPT-4 throughput comparisons.
- What you can do after
- Apply via streaming, Markdown rendering, navigation controls, validation processes, and bias audits when building generative AI web applications.
Read how Build AI-Enhanced Web Apps explains user experience →
Natural Language Processing in Action, Second Edition
User experience is the overall quality of a person's interaction with a product or interface, including usability, satisfaction, and effectiveness.
- How it is used
- Evaluates chatbot performance through user satisfaction, engagement, learning, and goal accomplishment alongside NLU performance and organizational impact.
- How deep it goes
- Distinguishes superficial activity counts from HEART metrics and begins applying adoption measures to meaningful chatbot journeys.
- Examples
- Uses chatbot starts, questions, thank-yous, subscriptions, multi-message interactions, and a math education lesson funnel.
- What you can do after
- Evaluate chatbot UX by selecting meaningful events and organizing them into HEART metric families.
Read how Natural Language Processing in Action, Second Edition explains user experience →
Effective Conversational AI
User experience is the overall quality of a person's interaction with a product, service, or system, including how natural and easy the interaction feels.
- How it is used
- Uses user experience to guide conversational AI design toward useful, low-effort interactions and successful goal completion.
- How deep it goes
- Compares burdensome and improved flows and links complexity to business metrics, without detailing a full evaluation method.
- Examples
- Uses claim-status calls to contrast unnecessary choice with immediate answers, and a banking chatbot to illustrate missed personalization.
- What you can do after
- Evaluate conversational flows by spotting user burden and tracking opt-outs, escalations, task completion, and satisfaction.
Read how Effective Conversational AI explains user experience →
The Design of Web APIs, Second Edition
End-user experience is the overall quality of an application interaction as perceived by the people who use it.
- How it is used
- Frames API design as a determinant of end-user and developer experience across private, partner, and public applications.
- How deep it goes
- Explains UX from discovery and credentials through integration, reliability, errors, and UI effects, but does not provide implementation procedures.
- Examples
- Uses SMS API discovery, messaging errors, timeline responsiveness, subscription funnels, and money-transfer regressions as concrete UX cases.
- What you can do after
- Evaluate API choices for discoverability, integration effort, feedback clarity, reliability, interoperability, and downstream UI quality.
Read how The Design of Web APIs, Second Edition explains user experience →
Generative AI in Action
User experience is the overall perception and interaction quality a person has when using a product or system.
- How it is used
- Shapes how users interact with enterprise GenAI systems and participate in responsible handling of generated outputs.
- How deep it goes
- Explains design interventions and architectural relationships, but stops short of detailed interaction specifications or implementation code.
- Examples
- Uses review-and-edit workflows, citations, AI disclosure, predetermined responses, input/output limits, and embedded guidance.
- What you can do after
- Apply user-centric safeguards that help people inspect, correct, understand, and responsibly use AI-generated content.
Related concepts
- pipelinecontrasts with · A pipeline is an ordered or coordinated sequence of connected processing stages or tasks that passes data, work, or results toward an outcome.
- prompt engineeringproduced 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.
- developer experienceincludes · Developer experience is the overall quality and effectiveness of developers’ interactions with the tools, systems, resources, and workflows involved in building and maintaining software.
- authorizationconfigured by · Authorization is the determination and enforcement of rules governing which subjects may access resources or perform operations.
- data modalitycontrasts with · A data modality is a distinct form or channel in which information is represented, conveyed, or processed, such as text, images, or audio.
- contractmeasured by · An API contract is a specification of the operations, data, and behavioral expectations that govern interactions between software components.
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.