push a little further on that for the stem documents. Okay. So okay, so if you kind of think of everything is a kind of an modular way you can think of every synthetic data pipeline is composed of the same six components and so you have your seeds, your primary inputs, your metadata, your secondary inputs, your generator and function which can be an agent with tools or one you know, with some prompt with some prompt templates and then some supplementary functions like filters and validators and so on. And really you can compose just about all pipelines from like very simple to very to very expensive pipelines like this. And on on that note and kind of we've we have covered quite a bit of wide scope on the the axis complexity and you kind of can think of it if one end you have like the cheap scalable pipelines that have used smaller models and can get get with it because they're seed heavy examples of phrasing. And then on the other end you have more complex pipelines with a little more orchestration in the workflows. This is reserved when the we're building on something that's worth it. Educational data. But really it's this is how we're not blocked or limited by whatever teacher model can do. And this is how we can be ambitious in our synthetic data. Because the rule of thumb is if task is too hard for your model, then your model will start to fall on its face. Lose correctness, lose diversity. So break down the task, make it simpler. And yeah, I will give some examples of kind of shapes rather than just like something more concrete about how do we use this this modularity. And one shape is the formula writing is just rephrasing. We already talked about this. Multi-stage pipelines and multi-stage workflows. Basically this is what I also just said. You take a step and you break it down into multiple steps. You can aggregate the processing. Slowly build up the the generation. Example of this were if you wanted to generate a novel for example, it's you could generate one chapter at a time, but you could also, you know, take it a little slowly. One first like generate, you know, the setting, the character names, the character styles, the plot, you know, some twists. And then and then from there go into generate the chapters one by one. You will absolutely get a better novel. Um Okay, third is cross-domain porting which really is just like like moving from one mode to another. Example would be changing like translating code. Another example would be something we did which is take our math problems and convert them to code. The last one is multi-turn role. What I mean by that all I mean is that instead of having kind of a very one like very singular or or linear or even non-linear kind of view of things you have more of an iteration. This encapsulates pretty much everything. And and that like an example of that would be multi-turn chats when you have two agents talking to each other or a task evolution pipeline where, you know, you have a judge and an evolver going back and forth for some k amount of time. Um,