Chatbots & Conversational Design

This course teaches how to design effective chatbots and conversational experiences on top of LLMs. It covers conversation design, intents and flows, grounding and handoff, tone and persona, and evaluation. Learners finish able to design and test a chatbot that actually helps users.

What this course covers

The course runs across 8 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.

Foundations of LLM Conversation Design — Understand the shift from rigid decision-tree bots to dynamic LLM-driven conversations. Learn the core principles of user-centric turn-taking and context management.
Designing Intents and Conversational Flows — Map user goals to intents and design flexible dialog flows. Practice structuring conversations that can handle digressions and return gracefully to the main path.
Grounding LLMs and Managing System Prompts — Learn how to ground LLM responses using system instructions and external data sources (RAG) to prevent hallucinations and keep responses accurate.
Coding Dynamic Flows and State Management — Implement conversational state tracking, variables, and conditional logic programmatically to build a cohesive backend for your chatbot.
Designing Tone, Voice, and Persona — Define and implement a consistent brand persona. Learn how to adjust tone dynamically based on user sentiment and context.
Handling Edge Cases, Fallbacks, and Human Handoff — Design seamless escalation paths. Learn when and how to hand off a conversation from the AI chatbot to a live human agent smoothly.
Evaluating Chatbot Performance and User Experience — Set up evaluation frameworks to measure conversation quality, task completion rates, and alignment. Learn to run automated and manual test suites.
End-to-End Chatbot Simulation and Refinement — Deploy your chatbot to a test environment, run simulated user interactions to stress-test the system, and iterate on prompt design based on performance data.

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 Large Language Models (LLMs), Chatbot Development, Voice User Interface (VUI) Design, Conversational Design, Prompt Engineering.

Large Language Models (LLMs)
Chatbot Development
Voice User Interface (VUI) Design
Conversational Design
Prompt Engineering

Who it is for

Product, support, builders. The material is pitched at intermediate level, and takes roughly 120 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: Conversational AI 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, code lab, role play 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

AI for Customer Experience — Faster, higher-quality support and success

Frequently asked questions

How long does this course take?

It runs roughly 120 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-CHAT.