What is the best way to document your algorithm logic and assumptions for stakeholders and collaborators?

Documenting your algorithm's logic and assumptions is like creating a clear recipe for a special dish. It helps you explain how the dish is made so that anyone can follow the steps and get the same tasty result. Similarly, when you document your algorithm, you're explaining how it works and why you made certain choices. This helps others understand and use your algorithm correctly. It also makes it easier to check if the algorithm is doing what it's supposed to do and fix it if something goes wrong. In simple terms, documenting your algorithm is like writing down the recipe for your computer program so that everyone can cook it up the same way and enjoy the desired outcome.

One of my favorite tools for documenting Mermaid.js, which lets you use a domain specific language (DSL) to generate diagrams, network graphs, UML, and other graphs and charts. The DSL can be generated from a script or via a GenAI routine, which can in turn generate SVG or PNG diagrams dynamically. This means that documentation specific graphics can be altered then updated with a click of a button, reducing the overall complexity of maintaining documentation. Mermaid diagrams can also be embedded within markdown content and stored concisely, eliminating the need of individually updating content.

To document your algorithm in simple terms, use clear language and explain step-by-step what it does and why. List assumptions and edge cases, provide examples and use code comments. For tools, consider using plain text files, Markdown, or specialized documentation software like Sphinx or Jupyter Notebook for Python algorithms.

For documenting algorithm logic and assumptions, include: 1. Overview: Describe what the algorithm does. 2. Purpose: Explain why you're using this algorithm. 3. Step-by-step: Break down the algorithm into clear steps. 4. Assumptions: List any conditions or data expectations. 5. Examples: Provide practical examples for clarity. 6. Edge Cases: Address unusual or extreme scenarios. 7. Code Comments: Explain code sections if applicable. 8. Updates: Keep documentation current with changes.

-Schedule regular reviews. -Collect feedback from users and team members. -Check for changes in algorithm logic. -Ensure clarity and correctness. -Correct errors and update examples. -Test the documentation for usability. -Use version control for tracking changes. -Archive old versions if possible. -Communicate significant updates to stakeholders. -Follow established documentation guidelines. -Train team members on documentation upkeep. Regularly maintain and improve your algorithm documentation to keep it accurate, clear, and helpful.

- Consider mentioning the value of documentation in preventing knowledge loss, facilitating onboarding of new team members, and aiding in troubleshooting and debugging. - You might want to add that using diagrams or visual aids can enhance understanding, and encouraging questions or feedback from readers can improve clarity. - Emphasize the importance of documenting not only the "what" and "how" but also the "why" behind decisions, as this provides context. - Mention that documenting the date of the last update can help users verify the freshness of the documentation, and consider automating documentation checks if possible.