Ship AI Features with Confidence: Type-Safe Prediction Objects

Building AI applications shouldn’t feel like walking a tightrope. Every time you deploy a new prompt or integrate an LLM response, you’re wondering: “Will this break in production?”

DSPy.rb eliminates that anxiety with type-safe prediction objects that catch errors during development, not when your users are watching.

The Problem: Runtime Surprises

Picture this: You’re building a content classification feature. Your LLM returns a confidence score, and everything works perfectly in development. But three weeks later, the model starts returning confidence as a string "0.95" instead of a float 0.95. Your production app crashes.

# This works in development...
result = classifier.call(content: "Amazing product!")
analytics.track_confidence(result.confidence * 100)  # Boom! 💥

# TypeError: String can't be coerced into Integer

Sound familiar? This is the reality of working with untyped LLM outputs.

The Solution: Know Your Data Structure

DSPy.rb prediction objects are fully typed structs that respond to both your input and output schema. When you define a signature, you get compile-time safety:

class ContentClassifier < DSPy::Signature
  description "Classify content sentiment and confidence"
  
  input do
    const :content, String
  end
  
  output do
    const :sentiment, String
    const :confidence, Float
  end
end

# Now your prediction object is type-safe
classifier = DSPy::Predict.new(ContentClassifier)
result = classifier.call(content: "Amazing product!")

# IDE autocomplete knows these exist
result.content      # ✅ "Amazing product!" (String)
result.sentiment    # ✅ "positive" (String)  
result.confidence   # ✅ 0.95 (Float)

Three Ways This Saves You Time

1. Catch Errors During Development

With Sorbet type checking enabled, integration errors surface immediately:

# This fails at type-check time, not runtime
def analyze_sentiment(text)
  result = classifier.call(content: text)
  
  # Sorbet catches this immediately
  result.nonexistent_field  # ❌ Type error: Method does not exist
  
  # And this type mismatch
  result.confidence + "high"  # ❌ Type error: Float + String
end

No more debugging mysterious crashes in production.

2. IDE Autocomplete That Actually Works

Your editor knows exactly what fields are available:

result = classifier.call(content: "Great service!")

# As you type 'result.', your IDE shows:
# - content (String)
# - sentiment (String)  
# - confidence (Float)

No more guessing field names or digging through documentation.

3. Self-Documenting Code

Prediction objects serve as living documentation:

def process_feedback(feedback_text)
  # The return type tells you everything you need to know
  result = classifier.call(content: feedback_text)
  
  # Input available for logging/debugging
  logger.info("Classified: #{result.content}")
  
  # Output fields are obvious
  if result.confidence > 0.8
    notify_team(result.sentiment)
  end
end

Real-World Example: Customer Feedback Pipeline

Here’s how a real customer feedback system benefits from type-safe predictions:

class FeedbackAnalyzer < DSPy::Signature
  description "Analyze customer feedback for sentiment and urgency"
  
  input do
    const :feedback, String
    const :customer_tier, String
  end
  
  output do
    const :sentiment, String
    const :urgency_score, Float
    const :suggested_action, String
  end
end

class FeedbackProcessor
  def process(feedback_text, tier)
    # Type-safe prediction
    analysis = analyzer.call(
      feedback: feedback_text,
      customer_tier: tier
    )
    
    # All fields are typed and available
    FeedbackReport.create!(
      original_feedback: analysis.feedback,        # Input field
      customer_tier: analysis.customer_tier,      # Input field
      sentiment: analysis.sentiment,               # Output field
      urgency: analysis.urgency_score,            # Output field  
      action: analysis.suggested_action           # Output field
    )
    
    # Type-safe conditional logic
    if analysis.urgency_score > 0.8
      alert_support_team(analysis)
    end
  end
end

The Hidden Benefit: Fearless Refactoring

When you need to modify your signature, the type system guides you through every change:

# Add a new output field
class FeedbackAnalyzer < DSPy::Signature
  # ... existing fields ...
  
  output do
    const :sentiment, String
    const :urgency_score, Float
    const :suggested_action, String
    const :category, String  # 👈 New field
  end
end

Sorbet immediately shows you every place that needs updating. No more hunting through your codebase wondering what might break.

Beyond Strings: Modeling Real-World Relationships

While strings work for simple examples, real applications have complex domain models. DSPy.rb signatures support rich types that model your actual business logic:

# Define your domain with T::Struct and T::Enum
class Priority < T::Enum
  enums do
    Low = new('low')
    Medium = new('medium')
    High = new('high')
    Critical = new('critical')
  end
end

class TicketDetails < T::Struct
  const :category, String
  const :priority, Priority
  const :estimated_hours, Float
  const :requires_escalation, T::Boolean
end

# Use rich types in your signatures
class TicketAnalyzer < DSPy::Signature
  description "Analyze support ticket for categorization and prioritization"
  
  input do
    const :ticket_content, String
    const :customer_tier, String
  end
  
  output do
    const :details, TicketDetails
    const :confidence, Float
  end
end

# Now your predictions return structured domain objects
analyzer = DSPy::Predict.new(TicketAnalyzer)
result = analyzer.call(
  ticket_content: "Critical database outage affecting all users",
  customer_tier: "enterprise"
)

# Type-safe access to nested properties
puts result.details.priority.serialize  # "critical"
puts result.details.requires_escalation  # true
puts result.details.estimated_hours      # 4.5

# Enum methods provide rich behavior
if result.details.priority == Priority::Critical
  escalate_immediately(result)
end

This approach brings several benefits:

Domain-Driven Design: Your AI outputs use the same types as your business logic Validation: Enums prevent invalid states like priority: "super-urgent" Behavior: Rich objects can have methods, not just data Refactoring: Change your domain model once, and all signatures adapt

Getting Started

Type-safe prediction objects work out of the box with DSPy.rb. Start simple and evolve toward richer domain models:

# 1. Start with basic types
class MySignature < DSPy::Signature
  input { const :question, String }
  output { const :answer, String }
end

# 2. Evolve to domain-specific types
class SupportTicket < T::Struct
  const :category, String
  const :urgency, Priority
end

class AdvancedSignature < DSPy::Signature
  input { const :ticket_text, String }
  output { const :analysis, SupportTicket }
end

# 3. Get type-safe results that match your domain
predictor = DSPy::Predict.new(AdvancedSignature)
result = predictor.call(ticket_text: "Login broken for all users")
puts result.analysis.urgency.serialize  # Fully typed and safe

The Bottom Line

Building AI features doesn’t have to be a guessing game. With DSPy.rb’s type-safe prediction objects, you get:

Immediate feedback on integration errors
IDE support that actually understands your data
Self-documenting code that’s easy to maintain
Confidence to ship features knowing they won’t break

Stop debugging runtime errors. Start building AI applications with the confidence that comes from knowing your data structures are correct.

Ready to experience type-safe AI development? Check out the DSPy.rb documentation and never worry about unexpected LLM outputs again.