Data-driven articles combine narrative text with visualizations to communicate insights but are time-consuming to produce, requiring both narrative composition and accurate text–chart linking. Existing automated linking methods struggle with accuracy, and automated narrative generation often misaligns with user intent and may contain hallucinations. We present VisCopilot, which leverages retrieval-augmented generation and large language models to provide fact-based, intent-aligned suggestions. As users write, the system supports narrative completion and text–chart linking in a single step, with a human-in-the-loop approach ensuring reliability. Technical evaluation on a benchmark dataset of data-driven articles authored by journalists confirmed the accuracy and quality of VisCopilot’s links. A user study further showed that VisCopilot outperforms baselines in usability, accuracy, and user confidence, with participants valuing its fact-based autocompletes for aligning suggestions with their writing intent. VisCopilot contributes: (1) an autocomplete-based design for assisting data-driven article authoring that balances efficiency, accuracy, and user autonomy; and (2) a reframing of text–chart link generation as a data-fact recommendation problem, enabling intent-aligned retrieval and systematic evaluation.