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Foundations of AI Interaction Building

Course Orientation and Outcomes

Interaction Design Principles for AI Systems

Intent Modeling and Data Foundations

Intent and Entity Modeling

Data Collection, Labeling, and Evaluation Sets

Prompt and Policy Strategy

Prompt Architecture and System Instructions

Safety, Compliance, and Governance Policies

Response Orchestration and Multichannel Delivery

Tooling, Orchestration, and State Management

Channel Integration and Multimodal Experiences

Evaluation, Experimentation, and Iteration

Quality Measurement and Experiment Design

Continuous Improvement Playbook

Hands-on Labs and Capstone

Lab: Build an End-to-End Interaction

Capstone: Production-Ready AI Interaction

Knowledge Checks

Quiz: Interaction Design and Intent Modeling

Quiz: Data and Evaluation Sets

Quiz: Prompt and Policy Strategy

Quiz: Orchestration and Multichannel Delivery

Quiz: Evaluation and Iteration

Wrap-up and Feedback

Course Summary and Next Steps

Learner Survey

AI Interaction Builder: From Intent Modeling to Orchestrated Responses is a practitioner-focused course that teaches you how to design, implement, and ship reliable, safe, and useful AI-powered experiences. You’ll learn the end-to-end interaction lifecycle—from identifying user intents and modeling entities, to crafting robust prompts and policies, to orchestrating tools and delivering responses across chat, voice, mobile, and web. Through a blend of principles, playbooks, and hands-on labs, you’ll master how to translate business goals into measurable interaction outcomes. The curriculum covers data foundations, safety and governance, response planning, and multichannel integration so you can build systems that are both delightful for users and dependable in production. You’ll instrument analytics, define success metrics, run A/B tests, and iterate with confidence using a continuous improvement framework. By the end, you will have built and evaluated a production-ready interaction, complete with guardrails, evaluation suites, and a handoff package. Whether you’re leveling up an existing assistant or launching a new AI capability, this course gives you the architecture, tactics, and templates to ship with speed—and scale with quality.

AI Interaction Builder: From Intent Modeling to Orchestrated Responses

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            <p>AI Interaction Builder: From Intent Modeling to Orchestrated Responses equips you to design, implement, and continuously improve AI-powered experiences that are reliable, useful, safe, and aligned to business goals. Starting with the foundations, you’ll learn human-centered interaction design principles—clarity, control, transparency, accessibility—and how to translate jobs-to-be-done into intents, entities, slots, and conversation maps. You’ll see the end-to-end lifecycle in practice: discover real user needs, design modality-aware flows across text, voice, and UI actions, implement with robust state and tool use, evaluate with meaningful metrics, and iterate with discipline.</p>

<p>The course builds a rigorous data and modeling spine before moving into prompt and policy strategy. You will construct intent and entity taxonomies, define disambiguation and success criteria, and design representative datasets with strong annotation guidelines, golden sets, adversarial cases, and drift detection. From there, you’ll develop layered prompting and system instructions, integrate tools and function-calling contracts, and encode safety and compliance via policies, classifiers, rules, and refusal patterns—supported by governance, audit trails, and human-in-the-loop practices.</p>

<p>On the execution side, you’ll master response orchestration with planner–executor patterns, multi-step tool use, memory and state management, determinism controls, and reliability mechanisms like fallbacks, retries, and circuit breakers. You’ll adapt interactions for multichannel delivery—chat, voice IVR, mobile, and web—while honoring latency budgets, streaming, barge-in, structured outputs, and analytics instrumentation for journey-level insights. Quality measurement is treated as a first-class practice: define success metrics such as accuracy, satisfaction, containment, and cost; combine human and model-based evaluations; run A/B tests and interleaving; and monitor post-release for regressions and drift.</p>

<p>Hands-on labs cement skills by guiding you through an end-to-end build: define intents and success criteria, author prompts and policies, implement orchestration with state and fallbacks, instrument metrics, and run an evaluation suite. The capstone pushes you to ship a production-ready, multichannel interaction with guardrails, experiments, and an operational handoff package—docs, dashboards, and runbooks. Knowledge checks throughout reinforce learning, and the wrap-up maps your skills to roles, platforms, and reusable playbooks so you can confidently lead AI interaction initiatives from first prototype to reliable production.</p>
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