DSPy.rb Expert

Overview

DSPy.rb is a Ruby framework that enables developers to program LLMs, not prompt them. Instead of manually crafting prompts, define application requirements through type-safe, composable modules that can be tested, optimized, and version-controlled like regular code.

This skill provides comprehensive guidance on:

• Creating type-safe signatures for LLM operations
• Building composable modules and workflows
• Configuring multiple LLM providers
• Implementing agents with tools
• Testing and optimizing LLM applications
• Production deployment patterns

Core Capabilities

1. Type-Safe Signatures

Create input/output contracts for LLM operations with runtime type checking.

When to use: Defining any LLM task, from simple classification to complex analysis.

Quick reference:

class EmailClassificationSignature < DSPy::Signature
  description "Classify customer support emails"

  input do
    const :email_subject, String
    const :email_body, String
  end

  output do
    const :category, T.enum(["Technical", "Billing", "General"])
    const :priority, T.enum(["Low", "Medium", "High"])
  end
end

Templates: See

assets/signature-template.rb

for comprehensive examples including:

• Basic signatures with multiple field types
• Vision signatures for multimodal tasks
• Sentiment analysis signatures
• Code generation signatures

Best practices:

• Always provide clear, specific descriptions
• Use enums for constrained outputs
• Include field descriptions with

  desc:

  parameter
• Prefer specific types over generic String when possible

Full documentation: See

references/core-concepts.md

sections on Signatures and Type Safety.

2. Composable Modules

Build reusable, chainable modules that encapsulate LLM operations.

When to use: Implementing any LLM-powered feature, especially complex multi-step workflows.

Quick reference:

class EmailProcessor < DSPy::Module
  def initialize
    super
    @classifier = DSPy::Predict.new(EmailClassificationSignature)
  end

  def forward(email_subject:, email_body:)
    @classifier.forward(
      email_subject: email_subject,
      email_body: email_body
    )
  end
end

Templates: See

assets/module-template.rb

for comprehensive examples including:

• Basic modules with single predictors
• Multi-step pipelines that chain modules
• Modules with conditional logic
• Error handling and retry patterns
• Stateful modules with history
• Caching implementations

Module composition: Chain modules together to create complex workflows:

class Pipeline < DSPy::Module
  def initialize
    super
    @step1 = Classifier.new
    @step2 = Analyzer.new
    @step3 = Responder.new
  end

  def forward(input)
    result1 = @step1.forward(input)
    result2 = @step2.forward(result1)
    @step3.forward(result2)
  end
end

Full documentation: See

references/core-concepts.md

sections on Modules and Module Composition.

3. Multiple Predictor Types

Choose the right predictor for your task:

Predict: Basic LLM inference with type-safe inputs/outputs

predictor = DSPy::Predict.new(TaskSignature)
result = predictor.forward(input: "data")

ChainOfThought: Adds automatic reasoning for improved accuracy

predictor = DSPy::ChainOfThought.new(TaskSignature)
result = predictor.forward(input: "data")
# Returns: { reasoning: "...", output: "..." }

ReAct: Tool-using agents with iterative reasoning

predictor = DSPy::ReAct.new(
  TaskSignature,
  tools: [SearchTool.new, CalculatorTool.new],
  max_iterations: 5
)

CodeAct: Dynamic code generation (requires

dspy-code_act

gem)

predictor = DSPy::CodeAct.new(TaskSignature)
result = predictor.forward(task: "Calculate factorial of 5")

When to use each:

• Predict: Simple tasks, classification, extraction
• ChainOfThought: Complex reasoning, analysis, multi-step thinking
• ReAct: Tasks requiring external tools (search, calculation, API calls)
• CodeAct: Tasks best solved with generated code

Full documentation: See

references/core-concepts.md

section on Predictors.

4. LLM Provider Configuration

Support for OpenAI, Anthropic Claude, Google Gemini, Ollama, and OpenRouter.

Quick configuration examples:

# OpenAI
DSPy.configure do |c|
  c.lm = DSPy::LM.new('openai/gpt-4o-mini',
    api_key: ENV['OPENAI_API_KEY'])
end

# Anthropic Claude
DSPy.configure do |c|
  c.lm = DSPy::LM.new('anthropic/claude-3-5-sonnet-20241022',
    api_key: ENV['ANTHROPIC_API_KEY'])
end

# Google Gemini
DSPy.configure do |c|
  c.lm = DSPy::LM.new('gemini/gemini-1.5-pro',
    api_key: ENV['GOOGLE_API_KEY'])
end

# Local Ollama (free, private)
DSPy.configure do |c|
  c.lm = DSPy::LM.new('ollama/llama3.1')
end

Templates: See

assets/config-template.rb

for comprehensive examples including:

• Environment-based configuration
• Multi-model setups for different tasks
• Configuration with observability (OpenTelemetry, Langfuse)
• Retry logic and fallback strategies
• Budget tracking
• Rails initializer patterns

Provider compatibility matrix:

Feature	OpenAI	Anthropic	Gemini	Ollama
Structured Output	✅	✅	✅	✅
Vision (Images)	✅	✅	✅	⚠️ Limited
Image URLs	✅	❌	❌	❌
Tool Calling	✅	✅	✅	Varies

Cost optimization strategy:

• Development: Ollama (free) or gpt-4o-mini (cheap)
• Testing: gpt-4o-mini with temperature=0.0
• Production simple tasks: gpt-4o-mini, claude-3-haiku, gemini-1.5-flash
• Production complex tasks: gpt-4o, claude-3-5-sonnet, gemini-1.5-pro

Full documentation: See

references/providers.md

for all configuration options, provider-specific features, and troubleshooting.

5. Multimodal & Vision Support

Process images alongside text using the unified

DSPy::Image

interface.

Quick reference:

class VisionSignature < DSPy::Signature
  description "Analyze image and answer questions"

  input do
    const :image, DSPy::Image
    const :question, String
  end

  output do
    const :answer, String
  end
end

predictor = DSPy::Predict.new(VisionSignature)
result = predictor.forward(
  image: DSPy::Image.from_file("path/to/image.jpg"),
  question: "What objects are visible?"
)

Image loading methods:

# From file
DSPy::Image.from_file("path/to/image.jpg")

# From URL (OpenAI only)
DSPy::Image.from_url("https://example.com/image.jpg")

# From base64
DSPy::Image.from_base64(base64_data, mime_type: "image/jpeg")

Provider support:

• OpenAI: Full support including URLs
• Anthropic, Gemini: Base64 or file loading only
• Ollama: Limited multimodal depending on model

Full documentation: See

references/core-concepts.md

section on Multimodal Support.

6. Testing LLM Applications

Write standard RSpec tests for LLM logic.

Quick reference:

RSpec.describe EmailClassifier do
  before do
    DSPy.configure do |c|
      c.lm = DSPy::LM.new('openai/gpt-4o-mini',
        api_key: ENV['OPENAI_API_KEY'])
    end
  end

  it 'classifies technical emails correctly' do
    classifier = EmailClassifier.new
    result = classifier.forward(
      email_subject: "Can't log in",
      email_body: "Unable to access account"
    )

    expect(result[:category]).to eq('Technical')
    expect(result[:priority]).to be_in(['High', 'Medium', 'Low'])
  end
end

Testing patterns:

• Mock LLM responses for unit tests
• Use VCR for deterministic API testing
• Test type safety and validation
• Test edge cases (empty inputs, special characters, long texts)
• Integration test complete workflows

Full documentation: See

references/optimization.md

section on Testing.

7. Optimization & Improvement

Automatically improve prompts and modules using optimization techniques.

MIPROv2 optimization:

require 'dspy/mipro'

# Define evaluation metric
def accuracy_metric(example, prediction)
  example[:expected_output][:category] == prediction[:category] ? 1.0 : 0.0
end

# Prepare training data
training_examples = [
  {
    input: { email_subject: "...", email_body: "..." },
    expected_output: { category: 'Technical' }
  },
  # More examples...
]

# Run optimization
optimizer = DSPy::MIPROv2.new(
  metric: method(:accuracy_metric),
  num_candidates: 10
)

optimized_module = optimizer.compile(
  EmailClassifier.new,
  trainset: training_examples
)

A/B testing different approaches:

# Test ChainOfThought vs ReAct
approach_a_score = evaluate_approach(ChainOfThoughtModule, test_set)
approach_b_score = evaluate_approach(ReActModule, test_set)

Full documentation: See

references/optimization.md

section on Optimization.

8. Observability & Monitoring

Track performance, token usage, and behavior in production.

OpenTelemetry integration:

require 'opentelemetry/sdk'

OpenTelemetry::SDK.configure do |c|
  c.service_name = 'my-dspy-app'
  c.use_all
end

# DSPy automatically creates traces

Langfuse tracing:

DSPy.configure do |c|
  c.lm = DSPy::LM.new('openai/gpt-4o-mini',
    api_key: ENV['OPENAI_API_KEY'])

  c.langfuse = {
    public_key: ENV['LANGFUSE_PUBLIC_KEY'],
    secret_key: ENV['LANGFUSE_SECRET_KEY']
  }
end

Custom monitoring:

• Token tracking
• Performance monitoring
• Error rate tracking
• Custom logging

Full documentation: See

references/optimization.md

section on Observability.

Quick Start Workflow

For New Projects

1. Install DSPy.rb and provider gems:

gem install dspy dspy-openai  # or dspy-anthropic, dspy-gemini

2. Configure LLM provider (see

   assets/config-template.rb

   ):

require 'dspy'

DSPy.configure do |c|
  c.lm = DSPy::LM.new('openai/gpt-4o-mini',
    api_key: ENV['OPENAI_API_KEY'])
end

3. Create a signature (see

   assets/signature-template.rb

   ):

class MySignature < DSPy::Signature
  description "Clear description of task"

  input do
    const :input_field, String, desc: "Description"
  end

  output do
    const :output_field, String, desc: "Description"
  end
end

4. Create a module (see

   assets/module-template.rb

   ):

class MyModule < DSPy::Module
  def initialize
    super
    @predictor = DSPy::Predict.new(MySignature)
  end

  def forward(input_field:)
    @predictor.forward(input_field: input_field)
  end
end

5. Use the module:

module_instance = MyModule.new
result = module_instance.forward(input_field: "test")
puts result[:output_field]

6. Add tests (see

   references/optimization.md

   ):

RSpec.describe MyModule do
  it 'produces expected output' do
    result = MyModule.new.forward(input_field: "test")
    expect(result[:output_field]).to be_a(String)
  end
end

For Rails Applications

1. Add to Gemfile:

gem 'dspy'
gem 'dspy-openai'  # or other provider

2. Create initializer at

   config/initializers/dspy.rb

   (see

   assets/config-template.rb

   for full example):

require 'dspy'

DSPy.configure do |c|
  c.lm = DSPy::LM.new('openai/gpt-4o-mini',
    api_key: ENV['OPENAI_API_KEY'])
end

3. Create modules in

   app/llm/

   directory:

# app/llm/email_classifier.rb
class EmailClassifier < DSPy::Module
  # Implementation here
end

4. Use in controllers/services:

class EmailsController < ApplicationController
  def classify
    classifier = EmailClassifier.new
    result = classifier.forward(
      email_subject: params[:subject],
      email_body: params[:body]
    )
    render json: result
  end
end

Common Patterns

Pattern: Multi-Step Analysis Pipeline

class AnalysisPipeline < DSPy::Module
  def initialize
    super
    @extract = DSPy::Predict.new(ExtractSignature)
    @analyze = DSPy::ChainOfThought.new(AnalyzeSignature)
    @summarize = DSPy::Predict.new(SummarizeSignature)
  end

  def forward(text:)
    extracted = @extract.forward(text: text)
    analyzed = @analyze.forward(data: extracted[:data])
    @summarize.forward(analysis: analyzed[:result])
  end
end

Pattern: Agent with Tools

class ResearchAgent < DSPy::Module
  def initialize
    super
    @agent = DSPy::ReAct.new(
      ResearchSignature,
      tools: [
        WebSearchTool.new,
        DatabaseQueryTool.new,
        SummarizerTool.new
      ],
      max_iterations: 10
    )
  end

  def forward(question:)
    @agent.forward(question: question)
  end
end

class WebSearchTool < DSPy::Tool
  def call(query:)
    results = perform_search(query)
    { results: results }
  end
end

Pattern: Conditional Routing

class SmartRouter < DSPy::Module
  def initialize
    super
    @classifier = DSPy::Predict.new(ClassifySignature)
    @simple_handler = SimpleModule.new
    @complex_handler = ComplexModule.new
  end

  def forward(input:)
    classification = @classifier.forward(text: input)

    if classification[:complexity] == 'Simple'
      @simple_handler.forward(input: input)
    else
      @complex_handler.forward(input: input)
    end
  end
end

Pattern: Retry with Fallback

class RobustModule < DSPy::Module
  MAX_RETRIES = 3

  def forward(input, retry_count: 0)
    begin
      @predictor.forward(input)
    rescue DSPy::ValidationError => e
      if retry_count < MAX_RETRIES
        sleep(2 ** retry_count)
        forward(input, retry_count: retry_count + 1)
      else
        # Fallback to default or raise
        raise
      end
    end
  end
end

Resources

This skill includes comprehensive reference materials and templates:

References (load as needed for detailed information)

• core-concepts.md: Complete guide to signatures, modules, predictors, multimodal support, and best practices
• providers.md: All LLM provider configurations, compatibility matrix, cost optimization, and troubleshooting
• optimization.md: Testing patterns, optimization techniques, observability setup, and monitoring

Assets (templates for quick starts)

• signature-template.rb: Examples of signatures including basic, vision, sentiment analysis, and code generation
• module-template.rb: Module patterns including pipelines, agents, error handling, caching, and state management
• config-template.rb: Configuration examples for all providers, environments, observability, and production patterns

When to Use This Skill

Trigger this skill when:

• Implementing LLM-powered features in Ruby applications
• Creating type-safe interfaces for AI operations
• Building agent systems with tool usage
• Setting up or troubleshooting LLM providers
• Optimizing prompts and improving accuracy
• Testing LLM functionality
• Adding observability to AI applications
• Converting from manual prompt engineering to programmatic approach
• Debugging DSPy.rb code or configuration issues