As LLMs become more integrated into data applications, the ability to generate chart configurations from natural language prompts is increasingly valuable. This article presents a frontend architecture that takes a user's natural language description, passes it through an LLM to produce a chart configuration (e.g., ECharts options), and then applies a validation layer to check for completeness, data type consistency, and rendering feasibility. The author discusses key challenges such as handling ambiguous prompts, ensuring the generated config matches available data fields, and providing fallback mechanisms when validation fails. This approach is particularly relevant for teams building AI-enhanced analytics dashboards where non-technical users need to create visualizations without learning charting APIs. The validation step is crucial to prevent silent rendering errors and to maintain user trust in AI-generated outputs.
A frontend implementation for converting natural language into validated chart configurations using LLMs, with a focus on correctness and real-world deployment.