Description:
In modern industrial and cloud-native environments, dashboards are essential tools for monitoring operations, analyzing system behavior, and supporting decision-making. They allow users to visualize data coming from machines, databases, APIs, and event streams in a clear and actionable way. However, designing and implementing dashboards is still often a manual and time-consuming activity that requires both domain knowledge and technical expertise in backend development, data querying, and frontend UI implementation.
This project aims to prototype a system that uses Large Language Models (LLMs) to automatically generate dashboards, or parts of dashboards, starting from human-friendly textual requests. The system will interpret natural language inputs such as “show machine efficiency trends over the last 24 hours” and combine them with structured data descriptions, including SQL schemas, JSON schemas, API definitions, or time-series metadata.
The focus is not only on generating visual components but also on building a controlled and reliable pipeline that can understand user intent, map it to available data sources, generate valid data queries, and produce reusable dashboard structures. The final objective is to explore how LLM-driven dashboard generation can reduce development effort while maintaining correctness, consistency, and safety in industrial contexts.
Why This System is Needed
Dashboards are widely used in industrial and software systems, but their creation usually requires several manual steps: understanding the user requirement, identifying the correct data source, writing queries, choosing the right visual representation, and implementing the frontend components. This process can be slow, repetitive, and difficult to adapt when requirements change frequently.
LLMs offer a promising way to simplify this workflow by translating natural language requests into structured outputs such as queries, layout definitions, charts, KPIs, filters, and reusable UI components. However, in industrial environments, generated results cannot be accepted without control. Queries must be correct and safe, visualizations must reflect the real meaning of the data, and the generated dashboard must remain predictable and usable.
For this reason, the system will not rely only on free-form generation. It will use schema-aware reasoning, validation mechanisms, and predefined component templates to ensure that generated dashboards are syntactically correct, semantically aligned with the available data model, and suitable for real-world operational scenarios.
How We Plan to Achieve It
To obtain acceptable results, the project has been divided into four main phases:
1. Requirements and Use Case Definition
The first phase involves identifying suitable dashboard generation scenarios in industrial or cloud-native environments. Typical use cases may include monitoring machine efficiency, visualizing telemetry data, analyzing system performance, or displaying operational KPIs over a selected time range.
This phase will also define the type of input the system must support, including natural language requests and structured data descriptions such as SQL schemas, JSON schemas, OpenAPI definitions, GraphQL schemas, or time-series metadata. The expected dashboard outputs will be defined as well, including charts, tables, filters, KPI cards, and layout configurations.
Particular attention will be given to ambiguity in natural language requests. The system must be able to detect when a request is incomplete, map user intent to the available data model, and apply validation rules before producing executable queries or UI components.
2. System Architecture Design
Based on the initial analysis, the overall architecture of the LLM-driven dashboard generation system will be designed. The architecture will include a natural language interpretation layer, a schema analysis and metadata extraction module, a query generation module, a dashboard layout generation module, and a validation layer.
The backend will be designed using technologies such as Go or Node.js, while the frontend prototype may be implemented using Angular or React. The data layer may rely on PostgreSQL, a time-series database, or structured API-based data sources. Optional technologies such as JSON Schema, OpenAPI, and GraphQL may be used to improve the system’s ability to reason over available data structures.
The architecture will also define how different generation strategies can be evaluated. These may include direct prompting, tool-based generation, or agent-based workflows. The goal is to compare flexibility, reliability, latency, and maintainability across different approaches.
3. Prototype Implementation
During this phase, a working prototype will be developed. The system will accept a natural language dashboard request together with one or more structured data descriptions. The LLM will interpret the request, identify the relevant entities and metrics, and map them to the available schema or API metadata.
After intent interpretation, the system will generate the required data queries, such as SQL or GraphQL queries, and propose a dashboard structure composed of charts, tables, KPI cards, filters, and layout sections. Where applicable, the prototype may also generate executable frontend components or reusable configuration files for Angular or React.
A validation pipeline will be implemented to check the correctness of generated outputs before they are used. This includes verifying query syntax, checking alignment with the data schema, preventing unsafe or unsupported operations, and ensuring that generated UI components follow predefined templates. The system may also include a feedback loop where users can refine the generated dashboard through additional natural language instructions.
4. Testing, Evaluation, and Documentation
The prototype will be tested using realistic dashboard generation scenarios. Evaluation will focus on the accuracy of intent interpretation, the correctness of generated queries, the usability of the generated dashboards, and the robustness of the validation mechanisms.
Different types of requests will be tested, including clear requests, ambiguous requests, requests with missing parameters, and requests involving multiple data sources. The system will also be evaluated in terms of latency, cost of LLM inference, frontend consistency, and the ability to generate reusable dashboard components.
The final documentation will describe the system architecture, supported input formats, generation workflow, validation rules, prototype implementation, and main limitations. It will also include guidelines for integrating LLM-driven dashboard generation into industrial systems, with possible extensions such as voice-based dashboard creation, real-time IoT data integration, continuous user feedback, and role-based dashboard customization.
Project Timeline
- Requirements and Use Case Definition: 40–50 hours
- System Architecture Design: 80–90 hours
- Prototype Implementation: 100–120 hours
- Testing, Evaluation, and Documentation: 50–60 hours
Total Time Frame: 270-330 hours
A possible Master’s-level extension of this project is the development of a context-aware Human-in-the-Loop agent for advanced dashboard and chart generation. The agent would help users refine ambiguous requests, apply domain knowledge such as KPI definitions and machine metadata, and generate validated outputs including charts, dashboards, and CSV/JSON data extraction queries. This extension would add an intelligent orchestration layer, clarification workflow, domain context store, and validation mechanisms to ensure that generated visualizations and queries are accurate, safe, and semantically consistent.