Curriculum
22 modules, about 4 h 48 min of reading plus exercises and knowledge checks. You’ve completed 0 so far (saved in this browser only).
- 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.