> 2. Start with a good spec. Spend enough time on the spec. You can get AI to write most of the spec for you, if you provide a good outline.
Curious how you outline the spec, concretely. A sister markdown document? How detailed is it? etc.
> 3. Make sure you have tests from the beginning. This is the most important part. Tests (along with good specs) are how an AI agent can recurse into a good solution. TDD is back.
Ironically i've been struggling with this. For best results i've found claude to do best with a test hook, but then claude loses the ability to write tests before code works to validate bugs/assumptions, it just starts auto fixing things and can get a bit wonky.
It helps immensely to ensure it doesn't forget anything or abandon anything, but it's equally harmful at certain design/prototype stages. I've taken to having a flag where i can enable/disable the test behavior lol.
> Curious how you outline the spec, concretely. A sister markdown document? How detailed is it? etc.
Yes. I write the outline in markdown. And then get AI to flesh it out. The I generate a project structure, with stubbed API signatures. Then I keep refining until I've achieved a good level of detail - including full API signatures and database schemas.
> Ironically i've been struggling with this. For best results i've found claude to do best with a test hook, but then claude loses the ability to write tests before code works to validate bugs/assumptions, it just starts auto fixing things and can get a bit wonky.
I generate a somewhat basic prototype first. At which point I have a good spec, and a good project structure, API and db schemas. Then continuously refine the tests and code. Like I was saying, types and linting are also very helpful.
I don't even write the outline myself. I tell CC to come up with a plan, and then we iterate on that together with CC and I might also give it to Gemini for review and tell CC to apply Gemini's suggestions.
I built a tool for this exact workflow in mind but with MCP and versioning included so you can easily track and edit the files on any platform including cursor, Claude desktop etc
What kind or projects are more suitable for this approach? Because my workflow, sans LLM agents, have been to rely on frameworks to provide a base abstraction for me to build upon. The hardest is to nail down the business domain, done over rounds of discussions with stakeholders. Coding is pretty breezy in comparison.
That's why you see such a difference in time saved using LLMs for programming across the population. If you have all the domain knowledge and the problem is generic enough it's a 100x multiplier. Otherwise your experience can easily range from 0.1x to 10x.
CLI for the humans, MCP for the LLMS. Whatever is in the context repository should be used by the LLM for its next steps and you are both responsible for maintaining it as tasks start getting done and the project evolves
I have a similar flow to this, but using files which are part of the repository. For you tool; What made you choose to version it with git but also write context is not code? Wouldn't you end up with multiple versions of say your backlog, and to what benefit?
The way I see it is that context evolves somewhat orthogonally to code. But you still want to track it in similar ways. Having git under the hood makes it easy to track its evolution and undo/diff things LLMs might decide to change, but also means that tracking your todos and new feature ideas don’t pollute your codebase
I will start with a basic markdown outline and then use a prompt describing more of the system in just flowing (yet coherent) thought and, crucially, I'll ask the model to "Organize the spec in such a way that an LLM can best understand it and make use of it." The result is a much more succinct document with all the important pieces.
(or -- you can write a spec that is still more fleshed out for humans, if you need to present this to managers. Then ask the LLM to write a separate spec document that is tailored for LLMs)
Playwright is such a chore with Claude but I'm afraid to live without it. Every feature seems to be about 70% of the time spent fixing it's playwright mess. It struggles with running the tests, basic data setup and cleanup, auth and just basic best practices. I have a testing guide that outlines all this but it half asses every thing ..
Curious how you outline the spec, concretely. A sister markdown document? How detailed is it? etc.
> 3. Make sure you have tests from the beginning. This is the most important part. Tests (along with good specs) are how an AI agent can recurse into a good solution. TDD is back.
Ironically i've been struggling with this. For best results i've found claude to do best with a test hook, but then claude loses the ability to write tests before code works to validate bugs/assumptions, it just starts auto fixing things and can get a bit wonky.
It helps immensely to ensure it doesn't forget anything or abandon anything, but it's equally harmful at certain design/prototype stages. I've taken to having a flag where i can enable/disable the test behavior lol.