Visualize NLU, LLMs, dialogue policies, memory, and channel adapters for conversations
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Design omnichannel chatbots with routing, NLP/LLM, analytics, and system integrations
Add safety layers to LLMs with content filters, policies, approvals, and audit logs
Map planners, tool execution, retrieval, and UI embedding for in-app copilots
Common questions about ai conversational architecture generator
Route known tasks to scripted flows and fallback to LLM with guardrails for unknown intents. Keep a shared state store for both paths.
Use short-term session memory and long-term vector or key-value stores. Prune and summarize to fit context windows.
Add language detection, translation, and locale-specific prompts. Store user locale and apply localized content and policies.
Add moderation on input/output, profanity filters, rate limits, and PII scrubbing. Include audit logs.
Track goal completion, sentiment, turn counts, fallback rates, and hallucination flags. Run spot checks with labeled transcripts.
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