data2prompt v0.5.0 fixes silent data gaps that cause LLMs to hallucinate on CSV samples

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Developer Aman Singh released v0.5.0 of data2prompt, a tool that converts large data files into LLM-readable single documents. The key update was not a new feature but a formal output contract ensuring that every time data is sampled or truncated, the original full count is captured and attached to the sample as a structured notice. Without this, LLMs treat small random samples as complete datasets and confidently generate false statistics or trends. The contract enforces a single consistent annotation grammar so the model can reliably distinguish tool metadata from actual file content. Writing the contract also uncovered real bugs in the codebase, including a faulty Excel chart-detection check triggered by read-only worksheet mode.

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