AIDD - Artificial Intelligence Driven Development, or GAIDD

Artificial Intelligence Driven Development (Generative Artificial Intelligence Driven Development) - A set of guiding principles for deploying AI technology in software development projects is intended to maximize the effectiveness of generative AI.

These were conceived based on first-hand experience in introducing AI into various projects involving over 100+ specialists.

Standardize artefact templates across the project, stream, and account. Pay keen attention to the requirements from software developers and QA’s regarding these artefacts. By having appropriately formatted artefacts, testers and developers can leverage AI more efficiently by bypassing any additional data transformations.
Rather than mass incorporating references and prototypes in tasks, endeavor to create independent, self-sufficient artifacts.
Adjust the Definition of Ready in line with proposed changes to the artefacts.
Introduce task management systems for non-technical specialists. This can include Kanban boards for business analysts, product managers, and owners, as well as scrum masters.
Establish a KPI system based on data from task tracking systems. Circumvent time logging, instead measure the duration of tasks in various statuses and the number of tasks in the pipeline in relation to the complexity of the task.
Prioritize the monitoring of important aspects of artefacts while minimizing unnecessary attention to less significant information. Avoid overemphasizing the need for perfectly polished requirements for development tasks.
Architect end-to-end generative AI use cases tailored for each type of specialist as well as for the complete development pipeline.
Eliminate scenarios that lead to a drop in performance and concentrate on executing highly efficient cases.
Foment a culture of AI utilization in the organization by constituting an AI guild. This would involve training specialists, investigating new usage scenarios, expanding the tools library, among others.
Refine processes to optimize the application of AI tools.
Employ a combination of AI and automation tools (or customized automation) to automate routine tasks and processes.

References

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