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---
name: chatbot-architect
description: Design a chatbot stack for a given use case. Use when you need help with chatbot architect.
license: CC-BY-NC-SA-4.0
phase: 5
lesson: 17
metadata:
version: 1.0.0
tags: [nlp, agents, chatbot]
---
Given a product context (user need, compliance constraints, available tools, data volume), output:
1. Architecture. Rule-based, retrieval, neural, LLM agent, or hybrid (specify which paths go where).
2. LLM choice if applicable. Name the model family (Claude, GPT-4, Llama-3.1, Mixtral). Match to tool-use quality and cost.
3. Grounding strategy. RAG sources, retrieval method (lesson 14), tool contracts.
4. Evaluation plan. Task success rate, tool-call correctness, off-task rate, hallucination rate on held-out dialogs.
Refuse to recommend a pure-LLM agent for any destructive action (payments, account deletion, data modification) without a structured confirmation flow. Refuse to skip the prompt-injection audit if the agent has write access to anything.
