prismAId

Help & Development


Page Contents

Getting Help

If you need assistance with any prismAId tool, you can:

Common Issues

General Issues

Download Tool Issues

Convert Tool Issues

Review Tool Issues

Contributing

How to Contribute

We welcome contributions to improve any aspect of the prismAId toolkit, whether you’re fixing bugs, adding features, or enhancing documentation:

Guidelines

For detailed contribution guidelines, see our CONTRIBUTING.md and CODE_OF_CONDUCT.md.

Software Stack

prismAId is developed in Go, selected for its simplicity and efficiency with concurrent operations. We prioritize the latest stable Go releases to incorporate improvements.

Technical Foundation

prismAId leverages the alembica pure Go package to manage interactions with Large Language Models. This foundation allows us to concentrate on developing robust protocol-based information extraction tools while alembica handles the standardized communication with various LLMs through consistent JSON data schemas, ensuring reliability and interoperability across different AI services.

Toolkit Architecture

The prismAId toolkit is structured as a set of modular tools (Download, Convert, Review) that can be used together or independently:

Development Philosophy

Open Science Support

prismAId actively supports Open Science principles through:

  1. Transparency and Reproducibility
    • prismAId enhances transparency, making analyses understandable and reproducible, with consistent results across systematic reviews.
    • Detailed logs and records improve reproducibility.
  2. Accessibility and Collaboration
    • An open-source, openly licensed tool fostering collaboration and participation.
    • Long-term accessibility through Zenodo.
  3. Efficiency and Scalability
    • Efficient data handling enables timely, comprehensive reviews.
    • Modular tools allow flexible workflows adapted to different research needs.
  4. Quality and Accuracy
    • Explicit prompts define information clearly, ensuring consistent, reliable reviews.
    • Separate tools for each workflow step improve focus and quality.
  5. Ethics and Bias Reduction
    • Transparent design minimizes biases, with community oversight supporting ethical standards.
  6. Scientific Innovation
    • Standardized, reusable methods facilitate innovation, cumulative knowledge, and rapid knowledge dissemination.