Dialog Rails
Dialog rails execute during multi-turn conversations to enforce flow logic, steer dialogs, and apply policies across the entire interaction. Unlike input/output rails that check individual messages, dialog rails manage the conversation state and structure.
When Dialog Rails Execute
Dialog rails run throughout the conversation lifecycle:
User Input → Dialog Rails → LLM → Dialog Rails → Response
↓ ↓
Flow Control Policy Enforcement
Dialog rails use Colang flows to define conversation patterns, topic boundaries, and state management.
Key Concepts
Conversation Flows
Flows define the allowed patterns of interaction:
define user express greeting
"hi"
"hello"
"hey"
define bot express greeting
"Hello! How can I help you today?"
define flow greeting
user express greeting
bot express greeting
Topic Control
Restrict conversations to allowed topics:
define user ask general question
"What stocks should I buy?"
"Can you recommend a place to eat?"
"What's your name?"
"Can you tell me a joke?"
define flow handle general questions
user ask general question
bot provide response
Disallowed Topics
Block specific conversation topics:
define user ask about politics
"Who should I vote for?"
"What party will win the elections?"
define bot refuse politics
"I cannot discuss political topics."
define flow block politics
user ask about politics
bot refuse politics
stop
Built-in Dialog Rails
Flow Management
Control the progression of conversations:
define flow capabilities
user ask capabilities
bot inform capabilities
define flow
user express greeting
bot express greeting
define flow
user ask general question
bot provide response
State Management
Track conversation state across turns:
define flow track user preferences
user provide preference
$user_preference = $user_message
bot acknowledge preference
define flow use stored preference
user request recommendation
if $user_preference
bot provide personalized recommendation
else
bot ask for preference
Policy Enforcement
Apply business rules throughout the conversation:
define flow enforce authentication
user request sensitive action
if not $authenticated
bot request authentication
user provide credentials
$authenticated = execute validate_credentials
if $authenticated
bot perform sensitive action
else
bot refuse action
Common Patterns
Guided Conversations
Lead users through structured interactions:
define flow onboarding
bot ask name
user provide name
$user_name = $user_message
bot ask email
user provide email
$user_email = $user_message
bot confirm registration
Context Tracking
Maintain conversation context:
define flow answer with context
user ask question
# Track conversation history
$conversation_history = $conversation_history + [$user_message]
bot provide contextual answer
Conditional Flows
Adapt based on conversation state:
define flow handle request
user make request
if $user_tier == "premium"
bot provide premium service
else
bot provide standard service
bot suggest upgrade
Advanced Dialog Rails
Subflows
Reusable conversation components:
define subflow verify identity
bot ask for verification
user provide verification
$verified = execute check_identity
if not $verified
bot inform verification failed
return False
return True
define flow sensitive operation
user request sensitive operation
$identity_verified = execute verify_identity
if $identity_verified
bot perform operation
else
bot refuse operation
Event Handling
Respond to system events:
define flow handle timeout
wait 300 # 5 minutes
bot ask if still there
user respond
or
# No response after 30 seconds
wait 30
bot inform session timeout
stop
Multi-Intent Handling
Manage complex user inputs:
define flow handle multiple intents
user express multiple intents
$intents = execute extract_intents
for $intent in $intents
bot address intent
Integration with Other Rails
Apply input rails based on dialog state:
define flow handle medical question
user ask medical question
# Extra validation for medical queries
$is_safe = execute content_safety_check_input(model="medical")
if $is_safe
bot provide medical answer
else
bot refuse unsafe medical question
Conditional Output Rails
Validate outputs based on topic:
define flow answer sensitive question
user ask sensitive question
bot provide answer
# Check answer for policy violations
$is_compliant = execute check_compliance
if not $is_compliant
bot provide alternative answer
Real-World Examples
Customer Support Flow
define flow customer support
bot greet customer
user describe issue
$issue_category = execute categorize_issue
if $issue_category == "technical"
execute technical_support_flow
else if $issue_category == "billing"
execute billing_support_flow
else
execute general_support_flow
define flow technical_support_flow
bot ask for error details
user provide error details
$solution = execute search_knowledge_base
if $solution
bot provide solution
bot ask if resolved
user confirm resolved
bot close ticket
else
bot escalate to engineer
Educational Tutoring
define flow tutoring session
bot ask current topic
user specify topic
$current_topic = $user_message
bot explain concept
bot ask if understood
user confirm understanding
or
user express confusion
bot provide alternative explanation
bot give example
Compliance Enforcement
define flow enforce compliance
user ask question
bot generate answer
# Check for regulatory compliance
$compliant = execute check_regulatory_compliance
$contains_legal_advice = execute detect_legal_advice
if not $compliant or $contains_legal_advice
bot provide disclaimer
bot suggest human expert
stop
Configuration
Enable Dialog Rails
Dialog rails are enabled by defining flows in .co files:
# config.yml
colang_version: "2.x"
instructions:
- type: general
content: |
You are a helpful assistant that follows conversation flows.
# flows.co
define flow
user express greeting
bot express greeting
define flow
user ask question
bot provide answer
Flow Priority
Control which flows take precedence:
define flow high_priority_flow
priority 10
user urgent request
bot immediate response
define flow normal_flow
priority 5
user normal request
bot normal response
Best Practices
- Keep flows focused - Each flow should handle a single conversation pattern
- Use subflows for reusability - Extract common patterns into reusable subflows
- Handle edge cases - Always provide fallback flows for unexpected inputs
- Track important state - Use variables to maintain conversation context
- Test flow coverage - Ensure all user intents have corresponding flows
- Document flow logic - Add comments explaining complex flow decisions
Limitations
Dialog Rails and Reasoning Models: Using advanced reasoning models (like OpenAI o1) with dialog rails is not currently supported. Reasoning models may not follow Colang flow patterns reliably.
- Flow matching overhead - Complex flow definitions add latency
- State management - Tracking many variables increases memory usage
- Intent recognition - LLM calls for intent matching add cost
Optimizations:
rails:
config:
# Cache intent recognition results
intent_cache:
enabled: true
max_size: 1000
Debugging Dialog Rails
Enable verbose logging:
logging:
level: DEBUG
show_llm_calls: true
show_internal_events: true
- Flow matching decisions
- State variable changes
- Event triggers
See Also