Design AI assistants people can trust.
A free, hands-on course in conversation design and LLM engineering: how to choose a model, ground it in your data, give it tools, orchestrate it, test it and keep it safe. Rebuilt for how assistants are actually made in 2026.
- 22modules
- 8case studies
- 4 h 48 minof study time
- 100+languages (machine translation)
- Freeno sign-up
Pick your path
Every module stands on its own, but you probably don’t need all 22 first. Choose a role for a suggested route.
Conversation designer
You shape what the assistant says, when it asks, how it recovers and how it sounds. Focus on principles, prompting, fallbacks, specs, voice and journeys; skim the code.
About 2 h 32 min of study time, plus the knowledge checks.
- 1Foundations of conversational AI9 min
- 2From intents to LLMs14 min
- 3Conversation design for LLM assistants11 min
- 5Prompting across different models17 min
- 11Fallbacks and graceful degradation13 min
- 13Inbound and outbound assistants: campaigns and messaging15 min
- 15Writing test case specs for AI15 min
- 18Safety, security and ethics10 min
- 19Voice assistants10 min
- 20Voice localization: STT and TTS across languages14 min
- 21Multimodal journeys13 min
- 22Capstone project and next steps11 min
Developer
You build the system. Focus on models, prompting, retrieval and ingestion, tools, orchestration, guardrails, fallbacks and evaluation, with working code.
About 2 h 38 min of study time, plus the knowledge checks.
- 2From intents to LLMs14 min
- 4The LLM landscape: choosing a model14 min
- 5Prompting across different models17 min
- 6Retrieval-augmented generation (RAG)12 min
- 7Preparing documents for ingestion18 min
- 8Tools, function calling and MCP14 min
- 9Orchestration: state machines, agents and multi-agent systems13 min
- 10Guardrails13 min
- 11Fallbacks and graceful degradation13 min
- 12Platforms and hybrid architectures9 min
- 14Evaluation and observability11 min
- 18Safety, security and ethics10 min
QA and tester
You decide whether it is safe and good enough to ship. Focus on guardrails, fallbacks, evaluation, specs, the QA playbook, locale testing and release gates.
About 2 h 22 min of study time, plus the knowledge checks.
- 1Foundations of conversational AI9 min
- 2From intents to LLMs14 min
- 10Guardrails13 min
- 11Fallbacks and graceful degradation13 min
- 14Evaluation and observability11 min
- 15Writing test case specs for AI15 min
- 16A QA playbook for testing AI17 min
- 17Data, analytics and BI for assistants15 min
- 18Safety, security and ethics10 min
- 20Voice localization: STT and TTS across languages14 min
- 22Capstone project and next steps11 min
Data and BI analyst
You turn conversations into insight. Focus on the event schema, metrics, dashboards, experiments, campaigns, privacy and retrieval metrics.
About 1 h 55 min of study time, plus the knowledge checks.
- 1Foundations of conversational AI9 min
- 4The LLM landscape: choosing a model14 min
- 6Retrieval-augmented generation (RAG)12 min
- 7Preparing documents for ingestion18 min
- 13Inbound and outbound assistants: campaigns and messaging15 min
- 14Evaluation and observability11 min
- 17Data, analytics and BI for assistants15 min
- 18Safety, security and ethics10 min
- 22Capstone project and next steps11 min
Content and knowledge owner
You own the documents the assistant answers from. Focus on retrieval, document design and ingestion, fallbacks and the metrics that reveal content gaps.
About 1 h 28 min of study time, plus the knowledge checks.
Product or CX leader
You decide what to build and how to measure it. Focus on the landscape, architecture, guardrails, channels, campaigns, metrics and real incidents.
About 2 h 15 min of study time, plus the knowledge checks.
- 1Foundations of conversational AI9 min
- 4The LLM landscape: choosing a model14 min
- 9Orchestration: state machines, agents and multi-agent systems13 min
- 10Guardrails13 min
- 11Fallbacks and graceful degradation13 min
- 12Platforms and hybrid architectures9 min
- 13Inbound and outbound assistants: campaigns and messaging15 min
- 17Data, analytics and BI for assistants15 min
- 18Safety, security and ethics10 min
- 21Multimodal journeys13 min
- 22Capstone project and next steps11 min
The curriculum
From first principles to production. Each module has worked examples, a knowledge check and sources you can verify.
- 01
Foundations of conversational AI
What an AI assistant actually is, what makes conversation hard to design, and how to tell whether one is working.
- 02
From intents to LLMs
How machines understand language, from intents and slots to embeddings and large language models, and when each approach still wins.
- 03
Conversation design for LLM assistants
Personas, system prompts as design documents, turn design, repair strategies and handoff: the craft that makes an assistant feel considered.
- 04
The LLM landscape: choosing a model
How to choose among proprietary and open models: criteria that matter for assistants, the tiers and routing strategies, and a dated snapshot of the families you will meet.
- 05
Prompting across different models
Why a prompt that works on one model fails on another, what actually differs, and how to keep prompts portable, versioned and tested.
- 06
Retrieval-augmented generation (RAG)
Ground your assistant in your own content: preparing knowledge, chunking, hybrid search, reranking, citations, evaluation and the failure modes to expect.
- 07
Preparing documents for ingestion
How to write, structure and manage source documents so retrieval works: single-document design, multi-document corpora, metadata, versions, format pitfalls and an ingestion pipeline with tests.
- 08
Tools, function calling and MCP
Let an assistant act: how tool calling works, how to design tools models can use safely, and what the July 2026 stateless MCP specification changes.
- 09
Orchestration: state machines, agents and multi-agent systems
Choose the right architecture: deterministic flows, a single tool-using agent or several cooperating agents, and how to combine them in a hybrid design.
- 10
Guardrails
Layered controls on inputs, retrieval, prompts, tools and outputs: what to enforce in code, what to ask of the model, how to tune false positives and how to test them.
- 11
Fallbacks and graceful degradation
Design what happens when understanding, retrieval, tools, models or channels fail: a fallback ladder, resilient calls, honest wording, handoff when no one is available and tests that break things on purpose.
- 12
Platforms and hybrid architectures
Cloud conversational services, open-source frameworks, visual builders and agent runtimes: how to choose, how to combine them, and how to migrate a legacy intent bot.
- 13
Inbound and outbound assistants: campaigns and messaging
Designing assistants that start conversations as well as answer them: consent and compliance, campaign and mailing design, two-way replies, voice outreach, architecture and the metrics that keep you honest.
- 14
Evaluation and observability
Build test sets, automated checks and LLM judges, then trace, monitor and gate every change so assistants improve instead of drifting.
- 15
Writing test case specs for AI
How to specify tests for assistants: fields, oracles, multi-turn scenarios, handling nondeterminism, coverage and review, with reusable templates.
- 16
A QA playbook for testing AI
How testers approach assistants: a test pyramid, exploratory charters, adversarial testing, flakiness, defect taxonomy, bug reports and release gates.
- 17
Data, analytics and BI for assistants
What to log, how to model it, which metrics to trust, how to build dashboards and experiments, and how to protect privacy while you learn from conversations.
- 18
Safety, security and ethics
The risks that matter for assistants: prompt injection, excessive agency, data exposure, honesty and accountability, mapped to the OWASP Top 10 for LLM applications.
- 19
Voice assistants
Design for the ear: voice architectures, latency budgets, barge-in, confirmation, repair and writing that sounds right when spoken.
- 20
Voice localization: STT and TTS across languages
Adapting speech recognition and synthesis to languages, dialects and domains: locales, vocabulary, normalization, SSML and lexicons, multilingual content, code-switching and testing per locale.
- 21
Multimodal journeys
Designing tasks that move across voice, chat, screens, images and documents: choosing the right modality, carrying context and identity, handling images and files, and testing cross-channel flows.
- 22
Capstone project and next steps
Apply everything: design, build, test and review a small assistant, with a brief, deliverables, a rubric and a plan for staying current.
Learn from real incidents
8 public cases, each traced to design decisions and what to do differently. All facts link to sources.
- 2024
When the chatbot's answer became the airline's problem
A tribunal held Air Canada responsible for wrong bereavement-fare advice from its own chatbot.
- 2024
The delivery chatbot that criticized its own company
After a system update, DPD's chat assistant swore at a customer and called DPD the worst delivery firm. The company switched off the AI part the same day.
- 2024
The city chatbot that told businesses to break the law
New York City's MyCity chatbot gave businesses incorrect and illegal guidance. The city added disclaimers, then shut it down in 2026.
- 2025
The assistant that handled two-thirds of chats, and the lesson about quality
Klarna's AI assistant took on a huge share of customer chats. Then the CEO said cost had been weighted too heavily, and the company began hiring humans again.
- 2025
The support bot that invented a policy to explain a bug
Cursor's AI support agent told a user that one device per subscription was a security policy. No such policy existed, and customers cancelled.
- 2024
The voice-ordering system that was switched off after viral mistakes
McDonald's ended its automated drive-thru ordering test with IBM after videos of order errors spread, and reports pointed to trouble with accents and dialects.
- 2025
The coding agent that deleted a production database during a code freeze
During a public experiment, an AI coding agent ran destructive commands against a live database despite a freeze, and then produced misleading output.
- 2024
When regulators said AI voices on the phone count as artificial voices
In February 2024 the US FCC ruled that AI-generated voices are artificial under the TCPA, bringing consent rules to AI outbound calls.
One-page cheat sheets
14 printable references, also available as PDF: prompts, RAG, tools and MCP, guardrails, test specs, metrics, voice, campaigns and more.
What changed since the first edition
The field moved fast. This rebuild reflects it, and each fast-moving page is dated.
Frequently asked questions
Is the course free, and do I need to sign up?
Yes, it is free and open to everyone. There is no account. Your progress is saved in your own browser on your device.
Who is this course for?
Conversation designers, developers, QA engineers and testers, data and BI analysts, and product or customer-experience leaders. Each role has a suggested path through the modules.
Do I need to know how to code?
No. Designers, QA, data and product readers can follow most of it without coding. Developers get working Python examples, including a tool-calling loop, an MCP server and a test runner.
How long does the course take?
The estimated study time is shown on every module and on the curriculum page. Plan for the reading plus the knowledge checks and the capstone exercises.
Which AI models does it cover?
It is model-agnostic. Module 4 gives you criteria and a dated snapshot of the main proprietary and open-weight families, and module 5 shows how prompts differ across models. Specific versions change often, so the course links to official sources.
Does it cover RAG, MCP and agents?
Yes. It covers retrieval-augmented generation, tool use and the Model Context Protocol (including the 2026-07-28 revision), and how to choose between state machines, single agents and multi-agent systems.
How does it teach testing and analytics?
There are dedicated modules on evaluation, writing test case specs, a QA playbook for AI, and data and BI for assistants, with templates, SQL examples and release-gate checklists.
Is it available in my language?
The course is written in English. You can read it in over 100 languages with the built-in machine-translation menu in the header, and code stays untouched. Machine translation can be imperfect, so use the original English for exact wording.
Are there cheat sheets?
Yes. There are one-page cheat sheets for prompts, RAG and ingestion, tools and MCP, guardrails and fallbacks, test case specs, QA, metrics, voice, campaigns and safety. View them online, print them or download the PDF.
How do I track my progress?
Answer the knowledge check at the end of each module. The progress widget saves what you have read and your quiz results, shows your next step, and lets you export your progress as a file.
How current is the content?
Fast-moving pages carry a review date, and facts link to official or primary sources. The course was reviewed on October 27, 2026.
Nelson Abacherli Kawakami
With more than two decades in design and digital innovation, Nelson has spent almost a decade specializing in conversational design, including chatbots, IVRs and AI systems, and has led creative and technical teams for 13 years. The course comes from that mix of design thinking and hands-on engineering.