What are the different types of AI chatbots?

There are three main types: Basic LLM chatbots (simple Q&A using model training data), RAG chatbots (enhanced with your documents for accurate answers about your business), and Agent chatbots (full automation that can take actions like booking appointments and updating records).

What is RAG in chatbots?

RAG (Retrieval-Augmented Generation) enhances chatbots by retrieving relevant information from your documents before generating responses. This allows the chatbot to answer questions about your specific business accurately, rather than relying solely on its training data.

How does chatbot human handoff work?

Human handoff occurs when the AI detects it should escalate to a human agent. Triggers include explicit user requests, detected frustration, complex issues, or VIP customers. The system generates a summary for the agent and seamlessly transfers the conversation with full context preserved.

What security features should a chatbot have?

Essential security features include PII redaction (automatically masking sensitive data), prompt injection protection, data encryption in transit and at rest, audit logging, and role-based access control. Enterprise deployments should also include compliance features for GDPR/HIPAA.

Deep Dive

Chatbot Architecture

How modern AI chatbots are built - from simple Q&A bots to fully autonomous agents that take actions on behalf of users.

Architecture Types

Processing Steps

∞

Channel Support

Foundations

Three Types of AI Chatbots

Not all chatbots are created equal. Understanding these three architectures helps you choose the right approach for your needs.

Basic LLM Chatbot

Direct conversation with an LLM. Simple to set up but limited to the model's training data.

Fast to implement

Good for general Q&A

No custom knowledge

Can't take actions

RAG Chatbot

Popular

LLM enhanced with your documents. Answers based on your actual content.

Uses your knowledge base

Accurate, sourced answers

Easy to update content

Read-only (no actions)

Agent Chatbot

Full autonomy with function calling. Can retrieve data AND take actions.

Books, updates, creates

Accesses live systems

Full task automation

More complex setup

Feature	Basic LLM	RAG Chatbot	Agent Chatbot
Knowledge source Where the bot gets information	LLM training data only	Your documents + LLM	Documents + External systems
Accuracy on your data Answers about your specific business	Low - often wrong	High - from your docs	High - plus real-time data
Can take actions Book, update, create, etc.
Setup complexity Time and effort to implement	Low	Medium	High
Best for Ideal use cases	General Q&A	Knowledge bases	Full automation

The final response is sent back to the user via their original channel. The conversation is saved to memory for future context.

Example

"Your order #12345 shipped and will arrive January 18th!"

Total Processing Time: ~700ms

A well-optimized chatbot can process messages in under a second. The LLM step is typically the slowest, but streaming responses make the experience feel faster.

Channels

Multi-Channel Architecture

Modern chatbots meet customers where they are. A unified architecture handles WhatsApp, web chat, SMS, and more through a single brain.

Rich media, quick replies

Web Widget

Embedded on your site

SMS

Universal reach

Messenger

Facebook integration

Unified conversation history

Start on WhatsApp, continue on web - context preserved

Single knowledge base

Update once, deploy everywhere

Channel-specific features

Rich media on WhatsApp, buttons on web

Channel Configuration Example

chatbot-config.json

1{
2  "channels": {
3    "whatsapp": {
4      "enabled": true,
5      "provider": "twilio",
6      "features": ["rich_media", "quick_replies", "location"]
7    },
8    "web_widget": {
9      "enabled": true,
10      "customization": {
11        "primary_color": "#1a73e8",
12        "position": "bottom-right"
13      }
14    },
15    "sms": {
16      "enabled": true,
17      "provider": "twilio",
18      "features": ["text_only"]
19    },
20    "facebook_messenger": {
21      "enabled": true,
22      "features": ["rich_media", "quick_replies", "persistent_menu"]
23    }
24  },
25  "unified_inbox": true,
26  "conversation_continuity": true
27}

Escalation

Human Handoff: When AI Steps Aside

The best chatbots know their limits. Seamless handoff to human agents ensures complex issues get proper attention.

Total: ~101ms

The AI recognizes triggers indicating a human should take over:

• User explicitly requests human ("talk to a person")
• Sentiment analysis detects frustration
• Complex issue outside AI's scope
• Multiple failed resolution attempts

AI generates a summary for the human agent:

Handoff Summary:

Customer frustrated about delayed order #12345. Shipping shows "in transit" for 5 days. Wants refund or express reshipping. High-value customer (3 previous orders).

Available agent receives notification with full context.

Routed based on: skill, availability, workload

Customer is smoothly connected to agent:

"I'm connecting you with Sarah from our support team. She has your order details and will help resolve this right away."

Automatic Escalation Triggers

• User explicitly requests human
• Negative sentiment detected (3+ messages)
• Issue marked as "high complexity"
• Failed to resolve after 3 attempts
• VIP customer flag
• Legal/compliance keywords detected

Handoff Best Practices

• Generate conversation summary
• Include customer sentiment score
• Pass user profile and history
• Skill-based agent routing
• Clear transition message to user
• Allow agent to see full transcript

Security

Security Considerations

Chatbots handle sensitive customer data. A secure architecture protects both your users and your business.

PII Protection

Automatically detect and redact credit card numbers, SSNs, and other sensitive data before it reaches the LLM. Never store raw PII in conversation logs.

Prompt Injection Defense

Users may try to manipulate the chatbot with crafted prompts. Implement input sanitization, output validation, and system prompt protection layers.

Data Handling

Encrypt all data in transit (TLS) and at rest. Implement data retention policies. Provide data export and deletion for GDPR compliance.

Security Features Comparison

Feature	Enterprise	Standard	Basic
PII redaction Automatically mask sensitive data
Prompt injection protection Block manipulation attempts
Data encryption (at rest) Encrypted storage
Data encryption (in transit) TLS/SSL connections
Audit logging Complete conversation logs
Role-based access Control who sees what

Enterprise solutions include comprehensive security features. For compliance-heavy industries, these are non-negotiable.

Full Picture

Complete Architecture Overview

How all the pieces fit together in a production chatbot system.

Channels

Web Chat

SMS

Core System

Message Router

LLM Engine

RAG Pipeline

Function Handler

Integrations

Vector DB

CRM/ERP

Help Desk

← Messages flow bidirectionally between all components →

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Chatbot Architecture

Three Types of AI Chatbots

Basic LLM Chatbot

RAG Chatbot

Agent Chatbot

The Conversation Processing Loop