Building Chatbots — the canonical assembly
The most-built, most-commoditized, most instructive product shape — because a good chatbot is the entire course in one stack:
The build sequence that works, in order: (1) Scope before anything — write the system prompt's escape hatches first (Topic 55): what this bot refuses, deflects, and escalates. Undefined scope is the root cause of most chatbot embarrassments. (2) Ground it — RAG over the real knowledge (Module 6), citations on, "say so if it's not in context" (Topic 48), golden set built the same week (Topic 75). (3) Manage the conversation — query rewriting for follow-ups (Topic 52, non-negotiable), history summarization before the quadratic cost curve bites (Topics 53/77). (4) Engineer the exits — the most underrated feature in the entire category: graceful human escalation. A bot that recognizes its limits and hands off with full context beats a bot that's 5% smarter and traps users; escalation rate and quality belong on the dashboard. (5) Capture feedback — 👍/👎 on every response: it's your eval signal (Topic 74) and your KTO training data (Topic 28) accumulating for free.
The chatbot-specific failure modes to design against: scope drift (users treat every chatbot as general-purpose ChatGPT — your refund bot will be asked for poetry and medical advice; the escape hatches are load-bearing), injection via retrieved content (Topic 57 — your own indexed documents are an attack surface), and measuring vanity — the metrics that matter are resolution rate, escalation rate, cost per conversation, and CSAT, never "messages sent" (engagement with a support bot is often a failure signal: users message more when unresolved).
And the honest market note: the chat interface is fully commoditized — anyone can ship one in a weekend. Differentiation lives entirely in layers 3–6 of the diagram: knowledge depth, integration into real systems (a bot that can actually check the order status via tools beats one that talks about it), and the eval-driven quality grind nobody sees.
Summary
Chatbot = the full course stacked: scoped constitution, grounded RAG, managed conversation, engineered escalation, captured feedback — measured by resolution and cost, differentiated by knowledge and tools, never by the chat window.
Mental model
A new support hire: worth exactly their training binder (RAG), their standing orders (system prompt), and their judgment about when to get the manager (escalation) — not their friendliness.
Mistakes to avoid
shipping without escalation ("the bot handles everything" is a promise you'll break publicly); and celebrating engagement metrics on a task-completion product.
Exercise
Assemble it — you've built every layer as separate exercises (Topics 48, 50, 52, 53, 55, 75). Wire them into one chatbot for a domain you know, with escalation as a literal escalate_to_human tool (Topic 56). Run your golden set through it. This is the course's first complete product.