FiddleCube – Generate Q&A to test your LLM
Convert your vector embeddings into a set of questions and their ideal responses. Use this dataset to test your LLM and catch failures caused by prompt or RAG updates. Get started in 3 lines of code: ``` pip3 install fiddlecube ``` ``` from fiddlecube import FiddleCube fc = FiddleCube(api_key="") dataset = fc.generate( [ "The cat did not want to be petted.", "The cat was not happy with the owner's behavior.", ], 10, ) dataset ``` Generate your API key: https://dashboard.fiddlecube.ai/api-key # Ideal QnA datasets for testing, eval and training LLMs Testing, evaluation or…
In plain words
FiddleCube generates question-and-answer datasets from vector embeddings to test and evaluate large language models. Developers use it to catch failures caused by prompt or retrieval-augmented generation updates. The tool creates diverse Q&A pairs from source texts and integrates with Python in three lines of code, providing a way to build golden datasets for LLM testing without manual effort.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Convert your vector embeddings into a set of questions and their ideal responses. Use this dataset to test your LLM and catch failures caused by prompt or RAG updates. Get started in 3 lines of code: ``` pip3 install fiddlecube ``` ``` from fiddlecube import FiddleCube fc = FiddleCube(api_key="<api-key>") dataset = fc.generate( [ "The cat did not want to be petted.", "The cat was not happy with the owner's behavior.", ], 10, ) dataset ``` Generate your API key: https://dashboard.fiddlecube.ai/api-key # Ideal QnA datasets for testing, eval and training LLMs Testing, evaluation or training LLMs requires an ideal QnA dataset aka the golden dataset. This dataset needs to be diverse, covering a wide range of queries with accurate responses. Creating such a dataset takes significant manual effort. As the prompt or RAG contexts are updated, which is nearly all the time for early applications, the dataset needs to be updated to match. # FiddleCube generates ideal QnA from vector embeddings - The questions cover the entire RAG knowledge corpus. - Complex reasoning, safety alignment and 5 other question types are generated. - Filtered for correctness, context relevance and style. - Auto-updated with prompt and RAG updates.
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