OneRange

Building AI Products

Learners study the patterns and pitfalls of shipping AI-powered products. The course covers use-case selection, UX for AI, reliability and trust, evaluation, and iteration and feedback loops. Participants finish able to build AI products that people trust and keep using.

What this course covers

The course runs across 12 topics, each one a short adaptive session rather than a recorded lecture. The tutor explains the idea, works an example, checks understanding, and adjusts the next step to the answer given.

  • Evaluating AI Feasibility and Use-Case Selection — Learn to distinguish between viable AI opportunities and cases where traditional heuristics are superior, using a business-value versus technical-feasibility matrix.
  • Mapping Data Readiness and Model Constraints — Assess context windows, data privacy requirements, and latency budgets to select the appropriate foundation model or API for your product.
  • Designing Intuitive AI User Experiences — Explore mental models for AI interaction, focusing on setting realistic user expectations and managing the 'uncanny valley' of AI assistance.
  • Managing Latency and Asynchronous States in UX — Implement UI design patterns like streaming responses, progress indicators, and optimistic UI updates to handle slow AI generation smoothly.
  • Designing for Errors, Hallucinations, and Uncertainty — Build fallback interfaces, confidence-score indicators, and human-in-the-loop validation checkpoints when AI outputs fail or hallucinate.
  • Establishing Reliability and Safety Guardrails — Configure moderation APIs, set up prompt-injection defenses, and establish system prompts to keep model behavior aligned with safety standards.
  • Building Trust through Explainability and Citations — Design mechanisms that show users the 'why' behind AI-generated decisions, including source citations and confidence level transparency.
  • Defining Evaluation Metrics and Ground Truth — Establish golden datasets, define grading criteria, and understand the trade-offs of using LLM-as-a-judge versus human evaluation.
  • Setting up Automated Evaluation Pipelines — Configure an evaluation tool to run automated regression tests on your prompts and models, measuring accuracy, bias, and tone.
  • Capturing User Feedback and Telemetry — Design explicit (thumbs up/down) and implicit (copy-to-clipboard, regenerations) feedback loops to capture real-world performance data.
  • Iterating on Prompts and Models Post-Launch — Analyze production logs to identify failure patterns, and run structured prompt engineering experiments to patch edge-case errors.
  • Continuous Monitoring and Drift Detection — Monitor live inputs and outputs for semantic drift, latency degradation, and cost spikes to maintain the long-term health of your AI product.

Skills you build

Results are measured against named skills in the OneRange taxonomy of more than 10,000 skills, so a manager sees proficiency per skill rather than a completion tick. This course maps to Identifying AI Use Cases, Product Strategy, Onboarding & Activation, AI Product Management, Responsible AI.

  • Identifying AI Use Cases
  • Product Strategy
  • Onboarding & Activation
  • AI Product Management
  • Responsible AI

Who it is for

PMs, founders, builders. The material is pitched at intermediate level, and takes roughly 150 minutes at a typical pace. Because every session adapts, someone who already knows a topic moves through it quickly instead of sitting through an explanation they do not need.

It sits in the Category: AI Products track of the Vero AI Catalog, and can be assigned to one person, a team, or the whole company.

How it is delivered and assessed

Delivery is conversational and interactive, including branching scenario, guided lab exercises. There is no video to sit through and no slide deck to click past.

Understanding is checked with a lab assessment of 5 items, with a pass mark of 70%.

Administrators can copy this course into their own library and adapt it — edit the outline, change the duration, swap the assessment format, or ground it in internal documentation so answers cite the company's own source material.

Also in this track

Related

Frequently asked questions

How long does this course take?

It runs roughly 150 minutes at a typical pace. Because every session adapts, someone who already knows a topic moves through it quickly instead of sitting through an explanation they do not need.

How is it assigned to a team?

An administrator assigns it from the Vero dashboard to one person, a team, a department or the whole company, and sees progress and results per person and per skill.

Can we customise it with our own documents and terminology?

Yes. Administrators can copy this course into their own library and adapt it — edit the outline, change the duration, swap the assessment format, or ground it in internal documentation so answers cite the company's own source material.

Course code CAT-PROD.