Chatbot Builder
VerifiedBuild conversational AI chatbots with intent classification, context memory, fallback handling, and integration with LLM APIs and messaging platforms.
Install
Claude Code
Copy the SKILL.md file to .claude/skills/chatbot-builder.md About This Skill
Chatbot Builder generates production-ready conversational AI systems with proper architecture for context management, intent handling, and multi-channel deployment.
Architecture
Three-layer design: Intent Router classifies incoming messages, Dialog Manager maintains conversation state and selects next action, Response Generator produces the final message via LLM or template.
Context Memory
Conversation history stored with sliding window (configurable turn limit). Summaries generated via LLM when window exceeds limit. Session metadata (user ID, channel, language) propagated through all layers.
Intent Classification
Hybrid approach: embedding-based semantic search for known intents, LLM fallback for novel queries. Intent confidence thresholds control escalation to human support.
RAG Integration
Document chunking and embedding pipeline for knowledge base Q&A. Retrieves top-k relevant chunks, injects into system prompt with source citations. Supports Pinecone, Weaviate, and pgvector backends.
Multi-Channel
Adapter pattern abstracts channel-specific message formats. Supports: Slack Bolt, WhatsApp Business API, Telegram Bot API, and embeddable web widget (React component). Single bot logic, multiple channel deployments.
Guardrails
Input content filtering, output safety checks, PII detection before logging, and rate limiting per user session.
Use Cases
- Building customer support bots with intent routing and escalation to human agents
- Creating FAQ bots with RAG over product documentation
- Implementing multi-turn conversation flows with session context persistence
- Deploying chatbots to Slack, WhatsApp, Telegram, and web widget simultaneously
Pros & Cons
Pros
- + RAG integration enables accurate Q&A over custom knowledge bases
- + Multi-channel adapter means one codebase deploys everywhere
- + Hybrid intent classification balances speed and accuracy
- + PII detection and content filtering built into the pipeline
Cons
- - RAG pipeline quality depends heavily on document chunking strategy
- - LLM API costs scale linearly with conversation volume
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