Skip to content

Roadmap

This page outlines the future development plans for the Prompt Decorators framework.

Implementation Phases

For a detailed breakdown of implementation status, see our Implementation Status document.

Core Framework Enhancements

  • Performance Optimization: Improve the efficiency of decorator application for large prompts
  • Expanded Decorator Library: Add 10+ new decorators covering additional prompt engineering techniques
  • Enhanced Compatibility Checking: Develop more sophisticated compatibility rules between decorators
  • Improved Error Handling: Add better error messages and recovery mechanisms

Documentation and Examples

  • Interactive Documentation: Create an interactive web-based playground for trying decorators
  • Video Tutorials: Produce a series of tutorial videos demonstrating key features
  • Case Studies: Document real-world use cases and success stories
  • Expanded Examples: Add more complex examples showing advanced usage patterns

Testing and Quality Assurance

  • Expanded Test Coverage: Achieve 95%+ test coverage across the codebase
  • Benchmark Suite: Develop benchmarks for measuring decorator performance
  • Compatibility Testing: Test with a wider range of LLM providers and models
  • Security Audit: Conduct a comprehensive security review

Advanced Features

  • Decorator Marketplace: Create a central repository for sharing community-created decorators
  • Visual Decorator Builder: Develop a GUI tool for creating and configuring decorators
  • Adaptive Decorators: Implement decorators that adapt based on the prompt content or context
  • Multilingual Support: Enhance decorators to work effectively with multiple languages
  • Versioned Registry: Support multiple versions of decorators in the registry

Integration and Ecosystem

  • LangChain Integration: Develop deeper integration with the LangChain ecosystem
  • Hugging Face Integration: Create specialized adapters for Hugging Face models
  • Web Framework Plugins: Build plugins for popular web frameworks (Flask, FastAPI, Django)
  • Vector Database Connectors: Add support for retrieving decorators from vector databases
  • Prompt Management System: Develop a system for managing and versioning decorated prompts

Analytics and Monitoring

  • Decorator Analytics: Add tools for tracking decorator usage and effectiveness
  • Performance Monitoring: Implement monitoring for decorator application time and token usage
  • A/B Testing Framework: Create tools for comparing different decorator combinations
  • Quality Metrics: Develop metrics for evaluating the quality of decorated prompts

Advanced AI Capabilities

  • Auto-Decorator Selection: Use AI to automatically select optimal decorators for a given prompt
  • Self-Optimizing Decorators: Implement decorators that learn and improve based on usage patterns
  • Context-Aware Decorators: Develop decorators that adapt based on conversation history
  • Multimodal Decorators: Extend the framework to support multimodal prompts (text + images)
  • Decorator Synthesis: Generate new decorators based on natural language descriptions

Enterprise Features

  • Role-Based Access Control: Add permissions for decorator creation and usage
  • Audit Logging: Implement comprehensive logging for compliance and security
  • Enterprise Deployment Tools: Create tools for deploying in enterprise environments
  • High Availability: Ensure the framework can operate in high-availability environments
  • SLA Monitoring: Add tools for monitoring service level agreements

Research and Innovation

  • Academic Partnerships: Collaborate with academic institutions on prompt engineering research
  • Published Research: Publish papers on the effectiveness of different decorator approaches
  • Novel Prompt Techniques: Research and implement cutting-edge prompt engineering techniques
  • Cross-Model Optimization: Develop techniques for optimizing prompts across different LLM architectures
  • Prompt Efficiency Research: Research methods for reducing token usage while maintaining effectiveness

Community and Ecosystem

  • Open Governance Model: Establish an open governance model for the project
  • Community Events: Host regular community events, hackathons, and challenges
  • Training and Certification: Develop training materials and certification programs
  • Extended Plugin System: Create a more robust plugin system for community extensions
  • Integration Ecosystem: Build a broader ecosystem of integrations with other AI tools

How to Contribute

We welcome contributions to help us achieve these roadmap goals! Here's how you can get involved:

  1. Feature Development: Pick an item from the roadmap and submit a pull request
  2. Bug Fixes: Help us address issues in the issue tracker
  3. Documentation: Improve our documentation or create tutorials
  4. Testing: Help us test the framework with different LLMs and use cases
  5. Feedback: Share your ideas and suggestions for the roadmap

See our Contributing Guide for more details on how to contribute.

Feedback

This roadmap is a living document and will evolve based on community feedback and changing priorities. If you have suggestions or feedback on the roadmap, please:

  • Open an issue on our GitHub repository
  • Discuss in our community forums
  • Reach out to the maintainers directly

We value your input in shaping the future of the Prompt Decorators framework!