Doesn't require Jira but yes, specification-first is the way to get better (albeit still not reliably good) results out of AI tools. Some people may call this "design-first" or "architecture-first". The point is really to think through what is being built before asking AI to write the implementation (i.e. code), and to review the code to make sure it matches the intended design.

Most people run into problems (with or without AI) when they write code without knowing what they're trying to create. Sometimes that's useful and fun and even necessary, to explore a problem space or toy with ideas. But eventually you have to settle on a design and implement it - or just end up with an unmaintainable mess of code (whether it's pure-human or AI-assisted mess doesn't matter lol).

I used to manually curate a whole set of .md files for specs, implementation logs, docs, etc. I operated like this for a year. In the end, I realized that I was rolling my own crappy version of Jira.

One of the key improvements for me when using Jira was that it has well defined patterns for all of these things, and Claude knows all about the various types of Jira tickets, and the patterns to use them.

Also, the spec driven approach is not enough in itself. The specs need sub-items, linked bug reports and fixes. I need comments on all of these tickets as we go with implementation decisions, commit SHAs, etc.

When I come back to some particular feature later, giving Claude the appropriate context in a way it knows how to use is super easy, and is a huge leap ahead in consistency.

I know I sound like some caveman talking about Jira here, but having Claude write and read from it really helped me out a lot.

It turns out that dumb ole Jira is an excellent "project memory" storage system for agentic coding tools.