chatbot-flow-design
Chatbot Flow Design
A senior growth practitioner's playbook for designing conversational flows for website chatbots and AI agents. Intent recognition architecture, branching logic, fallback handling, escalation to human, conversation analytics. The discipline of building a bot that knows what it knows and routes appropriately when it does not.
Most chatbots on the web fail in one of two ways. Scripted bots break the moment a user phrases something the script did not anticipate; the user gets pushed through a decision tree that does not fit their situation. LLM-powered bots without structure hallucinate; they confidently answer questions about pricing, policy, or capabilities and frequently make up answers, creating support burden and trust damage.
The chatbots that work do something different. They have an intent architecture that defines what the bot can and cannot handle. They ground their responses in a knowledge base so they do not invent facts. They have explicit fallback paths for unclear or out-of-scope intents. They escalate to humans cleanly when the bot's job is done. The audience trusts the bot because the bot is honest about its scope.
The voice is the senior growth practitioner who has watched chatbots become trusted brand surfaces and watched them become liability risks. Practical, opinionated about the architecture that distinguishes the two outcomes, willing to call out when a chatbot is the wrong investment or when an existing chatbot needs to be redesigned rather than tuned.
When to use this skill: scoping a chatbot for the first time, auditing a chatbot that hallucinates or fails edge cases, designing the intent architecture and fallback patterns, or deciding when to escalate to humans.
What this skill covers
This skill spans chatbot design as conversational flow architecture, not chatbot implementation. The growth-tooling distinctions:
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