Exploring Context and Continuity in AI-Assisted Development: The AI Dev Workflow CLI

AI-assisted development with large language models (LLMs) and agents has tremendous potential, but it comes with its own set of challenges. One persistent issue I’ve encountered is the lack of context continuity across sessions, which disrupts productivity and consistency. This inspired me to create the AI Dev Workflow CLI, an open-source tool designed to solve these problems by maintaining seamless context, ensuring consistency, and defining clear workflows.

In this post, I’ll share the problems I aimed to solve, the solutions implemented in the CLI, and the role of a documentation-first approach in AI-assisted development.

Each new session requires re-explaining the project to the assistant, often leaving you to hope it recalls where you left off.

• Solution: Persistent context management using commands like ai-dev status to pick up exactly where you left off.

Without defined guidelines, the AI writes in its own style, leading to inconsistencies that require extra fixes.

• Solution: Workflow tenets, style guidelines, and tagging to ensure the AI follows consistent patterns.

The lack of uniformity in AI-generated outputs across the project undermines quality and coherence.

• Solution: Context and workflow rules to align all outputs with the project’s expectations

To effectively tackle these problems, I’ve adopted a documentation-first approach. The AI can’t provide meaningful help if it doesn’t have clear insights about how the project should work. By documenting workflows, styles, and goals upfront, the AI becomes a much more effective collaborator.

The AI Dev Workflow CLI embodies this philosophy by prioritizing:

• Persistent documentation in .ai directory structures.
• Commands like ai-dev context to refresh or retrieve the documented rules.
• Consistent patterns are defined in YAML templates and annotations.

Funny enough, the project I chose to tackle first is the very project solving these issues. The CLI is effectively writing itself as I iterate and refine it with the help of AI. This meta-development approach demonstrates the power (and quirks!) of AI-assisted workflows.

What’s Next?

The CLI is for anyone exploring AI-assisted development. By improving AI literacy and simplifying workflows, I hope to make this tool accessible to writers, researchers, and non-coders. Future updates will focus on:

• Enhancing context management.
• Adding templates for non-developers.
• Expanding documentation for AI literacy.

Try It Yourself

The AI Dev Workflow CLI is open-source and built in public. You can check it out and contribute here:

Closing Thoughts:

The AI Dev Workflow CLI is my way of tackling the real challenges of AI-assisted development. It’s a work in progress, and while solving these issues would be a great perk, my main focus is on learning how to effectively collaborate with AI in development workflows.

If you’re curious or want to join me on this journey of exploration and learning, let’s connect and build this together!