# FAQ In this page we answer *frequently asked questions* about the library. We know these to be mostly pain points we need to address as soon as possible in the form of better features o better documentation. > How can I create a stream of experiences based on my own data? You can use the [Benchmark Generators](https://avalanche-api.continualai.org/en/v0.1.0/benchmarks.html#benchmark-generators): such utils in Avalanche allows you to build a stream of experiences based on an AvalancheDataset (or PyTorchDataset), or directly from PyTorch tensors, paths or filelists. > Why some Avalanche strategies do not work on my dataset? We cannot guarantee each strategy implemented in Avalanche will work in any possible setting. A continual learning algorithm implementation is accepted in Avalanche if it can reproduce at least a portion of the original paper results. In the [CL-Baseline](https://github.com/ContinualAI/reproducible-continual-learning) project we make sure reproducibility is maintained for those with every main avalanche release. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://avalanche.continualai.org/questions-and-issues/faq.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.