STAGE 2 PROMPT SUMMARY

Documentation for STAGE_2_PROMPT_SUMMARY from the Dspex repository.

Stage 2: Native Implementation - Comprehensive Prompt Strategy

Overview

After creating the detailed technical specification, it’s clear that the original 12 prompts are insufficient to cover the complexity and scope of the native Elixir DSPy implementation. The technical specification reveals significantly more components and integration points than originally anticipated.

Recommendation: Expand to 16 comprehensive prompts to properly handle all components with adequate detail and context.

Revised Prompt Architecture

Foundation Layer (Prompts 1-4)

Core infrastructure and compilation systems

Execution Layer (Prompts 5-8)

Module systems, prediction pipelines, and reasoning patterns

Provider Layer (Prompts 9-12)

External integrations, provider clients, and communication

Production Layer (Prompts 13-16)

Performance, monitoring, deployment, and integration

16 Comprehensive Stage 2 Prompts

Prompt 1: Native Signature Compilation System

File: prompts/stage2_01_signature_compilation.md Focus: Complete native signature compilation with ExDantic deep integration Key Components:

Native Elixir signature parsing and compilation
ExDantic schema generation and validation integration
Multi-provider JSON schema generation (OpenAI, Anthropic, generic)
High-performance ETS-based signature caching with intelligent eviction
Compile-time optimizations and analysis
Type inference and constraint validation

Required Context:

Complete DSPy signatures/signature.py analysis and patterns
ExDantic core architecture and schema creation patterns
Elixir macro system and compile-time processing
Provider-specific schema requirements and optimizations
Performance optimization techniques for compilation caching

Prompt 2: Advanced Type System and ML-Specific Types

File: prompts/stage2_02_type_system.md Focus: Comprehensive ML type registry with ExDantic integration Key Components:

ML-specific type definitions (reasoning_chain, confidence_score, embeddings, etc.)
Type validation pipeline with custom validators
Type coercion and conversion systems
Provider-specific type optimizations
Dynamic type registration and management
Quality assessment and metrics integration

Required Context:

Complete ExDantic type system and TypeAdapter patterns
DSPy type requirements and field definitions
ML-specific validation patterns and constraints
Provider API requirements for different data types
Performance considerations for type validation at scale

Prompt 3: Core Module System Architecture

File: prompts/stage2_03_module_system.md Focus: GenServer-based module system with state management Key Components:

Native DSPy module behavior using GenServer patterns
Parameter tracking and management systems
Module composition and dependency management
State persistence and recovery mechanisms
Module registry and lifecycle management
Supervision tree integration for fault tolerance

Required Context:

Complete DSPy primitives/module.py analysis and patterns
Advanced GenServer patterns for stateful ML operations
OTP supervision strategies for module management
Parameter tracking and optimization state management
Module composition and execution coordination

Prompt 4: Program Execution Engine

File: prompts/stage2_04_program_execution.md Focus: Advanced program orchestration and execution coordination Key Components:

Program execution graph and dependency resolution
Parallel module execution with coordination
Execution context and state management
Error handling and recovery strategies
Performance monitoring and optimization
Resource allocation and cleanup

Required Context:

DSPy primitives/program.py analysis and execution patterns
Task coordination and parallel execution in Elixir
Advanced GenServer coordination patterns
Error propagation and recovery strategies
Resource management for ML workloads

Prompt 5: Prediction Pipeline System

File: prompts/stage2_05_prediction_pipeline.md Focus: Core prediction execution with monitoring and optimization Key Components:

Native prediction engine with comprehensive execution strategies
Execution history tracking and analysis
Performance metrics collection and analysis
Multi-provider execution coordination
Adaptive execution strategy selection
Result validation and quality assessment

Required Context:

Complete DSPy predict/predict.py analysis and patterns
Prediction execution strategies and optimizations
Provider coordination and fallback mechanisms
Performance monitoring and telemetry integration
Quality assessment and result validation

Prompt 6: Chain-of-Thought Implementation

File: prompts/stage2_06_chain_of_thought.md Focus: Native CoT reasoning with step validation and quality assessment Key Components:

Enhanced signature generation for reasoning steps
Step-by-step reasoning validation and consistency checking
Reasoning quality assessment and scoring
Intermediate result handling and validation
Confidence calibration and uncertainty quantification
Reasoning chain optimization and improvement

Required Context:

DSPy predict/chain_of_thought.py analysis and patterns
Reasoning validation techniques and quality metrics
Step-by-step validation and consistency checking
Advanced prompt engineering for reasoning tasks
Quality assessment frameworks for reasoning chains

Prompt 7: React Pattern and Tool Integration

File: prompts/stage2_07_react_patterns.md Focus: React pattern implementation with tool calling and action execution Key Components:

React pattern execution with reasoning and action cycles
Tool integration and function calling capabilities
Action validation and execution management
Thought-action-observation cycle implementation
Error handling and recovery in multi-step workflows
Integration with external tools and APIs

Required Context:

DSPy predict/react.py analysis and implementation patterns
Tool calling and function execution patterns
Multi-step workflow orchestration
External API integration and error handling
Reasoning and action validation techniques

Prompt 8: Memory Management and Performance Optimization

File: prompts/stage2_08_memory_performance.md Focus: Advanced memory management and performance optimization for ML workloads Key Components:

Memory pressure detection and backpressure systems
Intelligent garbage collection strategies
ETS-based caching with memory-aware eviction
Large data streaming and chunked processing
Process pool management and scaling
Resource utilization monitoring and optimization

Required Context:

Advanced Elixir memory management patterns
ETS optimization strategies for large datasets
Streaming and chunked processing techniques
Process pool scaling and load balancing
Memory pressure detection and mitigation

Prompt 9: Provider Integration Framework

File: prompts/stage2_09_provider_framework.md Focus: Comprehensive provider integration with native HTTP clients Key Components:

Provider behavior definitions and contracts
Native HTTP client implementation with connection pooling
Provider-specific adapters (OpenAI, Anthropic, Google, etc.)
Request/response transformation and validation
Provider capability detection and optimization
Multi-provider coordination and fallback strategies

Required Context:

Complete DSPy adapters/ and clients/ analysis
Provider API specifications and requirements
HTTP client optimization and connection pooling
Provider-specific features and capabilities
Error handling and fallback strategies

Prompt 10: Circuit Breakers and Resilience Patterns

File: prompts/stage2_10_resilience_patterns.md Focus: Advanced fault tolerance and resilience for external provider dependencies Key Components:

Circuit breaker implementation with exponential backoff
Intelligent retry strategies with jitter and provider-specific logic
Rate limiting and request throttling
Health monitoring and automatic recovery
Provider failure detection and isolation
Graceful degradation strategies

Required Context:

Advanced fault tolerance patterns in distributed systems
Circuit breaker and retry strategy implementations
Rate limiting algorithms and fairness strategies
Health monitoring and failure detection
Provider-specific reliability characteristics

Prompt 11: Distributed Computing and Clustering

File: prompts/stage2_11_distributed_computing.md Focus: Multi-node coordination and distributed ML workload management Key Components:

Distributed execution coordination across cluster nodes
Node capability discovery and workload distribution
Distributed caching with consistency guarantees
Load balancing and failover mechanisms
Cluster health monitoring and automatic rebalancing
Network partition handling and recovery

Required Context:

Elixir distributed computing patterns and clustering
Distributed system consistency and coordination
Load balancing algorithms for ML workloads
Network partition tolerance and recovery
Cluster monitoring and health management

Prompt 12: Optimization and Teleprompt System

File: prompts/stage2_12_optimization_teleprompt.md Focus: Native optimization algorithms and prompt improvement systems Key Components:

Signature optimization and prompt tuning algorithms
Parameter optimization using Elixir-native approaches
Performance measurement and comparison frameworks
Automated prompt improvement and testing
Optimization history tracking and analysis
Multi-objective optimization strategies

Required Context:

Complete DSPy teleprompt/ analysis and optimization patterns
Optimization algorithm implementations in Elixir
Prompt engineering and improvement techniques
Performance measurement and comparison frameworks
Multi-objective optimization strategies

Prompt 13: Evaluation and Metrics Framework

File: prompts/stage2_13_evaluation_metrics.md Focus: Comprehensive evaluation system with ML-specific metrics Key Components:

Native evaluation engine with metric calculation
ML-specific metrics and quality assessment
Benchmark execution and comparison frameworks
Automated evaluation pipelines
Result analysis and visualization
Performance regression detection

Required Context:

Complete DSPy evaluate/ analysis and evaluation patterns
ML evaluation metrics and assessment techniques
Statistical analysis and comparison methods
Automated testing and evaluation pipelines
Performance monitoring and regression detection

Prompt 14: Streaming and Async Operations

File: prompts/stage2_14_streaming_async.md Focus: Real-time streaming and asynchronous ML operations Key Components:

Streaming response handling and processing
Asynchronous execution patterns with GenStage/Flow
Real-time data processing pipelines
Backpressure management for streaming workloads
Live result aggregation and transformation
WebSocket and Server-Sent Events integration

Required Context:

DSPy streaming/ analysis and streaming patterns
GenStage and Flow patterns for data processing
Streaming data handling and backpressure management
Real-time processing and aggregation techniques
WebSocket and SSE integration patterns

Prompt 15: Telemetry and Production Monitoring

File: prompts/stage2_15_telemetry_monitoring.md Focus: Comprehensive telemetry system with ML-specific metrics and alerting Key Components:

ML-specific telemetry events and metrics collection
Performance monitoring and anomaly detection
Alert rules and notification systems
Metrics aggregation and export to external systems
Dashboard integration and visualization
Cost tracking and resource utilization monitoring

Required Context:

Advanced Elixir telemetry patterns and monitoring
ML-specific metrics and KPIs
Alert systems and notification strategies
Metrics export and integration with monitoring systems
Cost tracking and resource optimization

Prompt 16: Complete Ash Framework Integration and Stage 2 Validation

File: prompts/stage2_16_ash_integration_validation.md Focus: Deep Ash integration and comprehensive Stage 2 completion validation Key Components:

Advanced Ash resource patterns for ML operations
Domain modeling and relationship management
Query engine integration and optimization
Action composition and workflow orchestration
Resource lifecycle management
Complete Stage 2 integration testing and validation
Performance benchmarking and production readiness assessment

Required Context:

Complete Ash framework advanced patterns and capabilities
Domain modeling for ML workflows and operations
Resource relationship management and optimization
Integration testing and validation strategies
Performance benchmarking and production readiness criteria

Prompt Design Principles

1. Complete Self-Containment

Each prompt contains ALL necessary context:

Relevant DSPy source code analysis copied in full
Complete ExDantic integration patterns and examples
Advanced Elixir/OTP implementation guidance
Provider-specific requirements and optimizations
Comprehensive testing and validation approaches

2. Deep Technical Integration

Each prompt provides:

Detailed implementation strategies combining all research
Complete code examples with error handling
Performance optimization techniques
Production readiness considerations
Integration with other Stage 2 components

3. Production Quality Focus

All implementations must include:

Comprehensive error handling and recovery
Performance monitoring and optimization
Security best practices and validation
Scalability and resource management
Operational monitoring and alerting

4. Incremental Complexity

Prompts build systematically:

Foundation layer establishes core infrastructure
Execution layer builds on foundation with complex workflows
Provider layer adds external integrations and resilience
Production layer completes with monitoring and deployment

Implementation Strategy

Phase 1: Foundation (Prompts 1-4)

Native signature compilation and type systems
Core module architecture and execution engine
Duration: 3-4 weeks
Success Criteria: Basic DSPy operations working natively

Phase 2: Execution (Prompts 5-8)

Prediction pipelines and reasoning patterns
Memory management and performance optimization
Duration: 3-4 weeks
Success Criteria: Complex ML workflows executing efficiently

Phase 3: Integration (Prompts 9-12)

Provider integrations and distributed computing
Optimization and resilience patterns
Duration: 3-4 weeks
Success Criteria: Production-ready external integrations

Phase 4: Production (Prompts 13-16)

Evaluation, monitoring, and Ash integration
Complete system validation and deployment
Duration: 3-4 weeks
Success Criteria: Full production deployment ready

Context Sources for Each Prompt

Research Foundation:

DSPy Analysis: Complete source code analysis from comprehensive research
ExDantic Integration: Deep integration patterns and advanced features
Elixir/OTP Patterns: Advanced patterns for ML workloads and high performance
Provider APIs: Complete specifications and optimization strategies

Implementation Guidance:

Performance Optimization: Memory management, caching, and scaling strategies
Error Handling: Comprehensive error recovery and fault tolerance
Monitoring: Telemetry, metrics, and operational observability
Testing: Validation, benchmarking, and quality assurance

This expanded 16-prompt strategy ensures comprehensive coverage of all components identified in the technical specification while maintaining manageable scope for each individual prompt.