Creating Web-Hosted Interactive Dashboards through Lovable

Dashboarding is perceived as a severe profession that requires some knowledge of statistics, a robust understanding of tools like Tableau or PowerBI, knowledge of programming languages like DAX, and M-code, and some significant analytical skills. This article provides a hands-on guide to creating a web-hosted dashboard, without the need to write even a single line of code.

Table of Contents

1. Dashboard design essentials
2. An introduction to lovable.dev
3. Best practices and common pitfalls in prompting
4. Step-by-step guide: No-code application building
5. Limitations of lovable.dev

Dashboard Design Essentials

Dashboarding is the art of presenting data through visualizations, making it easier to comprehend and extract insights in a clear and engaging format. A typical visualization project would consist of the following broad steps – 

Apart from these, there can be a requirement to create some custom ‘measures’ that involve programming. This article explores the automation of all the aforementioned steps with the help of A.I.-powered development tools e.g. ‘Lovable.dev’.

An introduction to lovable.dev

Lovable enables users to create full-stack web applications without requiring coding expertise, by simply describing the requirement in plain English. The user can keep refining and editing the application with a follow-up conversation on the go. The capabilities of lovable can be summarized in the following 6 points – 

Best Practices and Common Pitfalls in Prompting

Prompting is the art of communicating with AI systems to get meaningful and accurate outputs. How the prompts are formulated, directly influences the quality of the results. The more context the AI has, the better it can tailor its response while vague prompts yield vague answers. Mentioning the intended audience, tone, length, or domain always helps, and assigning a role can further narrow AI’s approach e.g. The AI can be asked to act as a teacher, engineer, subject matter expert, etc.

‘Structure’ is directly proportional to ‘clarity’. AI follows formatting cues very well, and it is always a good idea to use proper formatting options e.g. paragraphs, spaces, pointers, etc. Secondly, trying to do too much in one go often leads to jumbled responses, and it is suggested to start with a small instruction and load it incrementally with further prompts.

Step-by-step guide: No-Code Application Building

Step 1. Configuring the problem statement

To instruct the AI builder, first, the user needs to have a crystal clear understanding of the requirement. In this article, an application is created that facilitates the upload of a CSV file of the ‘World Billionaire’s dataset’. The dataset consists of features like rank, total worth, age, location, gender, business category, education level in their country, etc

The application should be able to create an interactive page that generates apt visualization for the following hypothesis –

• Age is an important determinant of wealth.
• People from countries with high literacy have a higher chance of becoming wealthy
• Sources of income for wealthy individuals are highly diverse
• Most of the billionaires are selfMade
• The GDP of the country has nothing to do with the literacy level
• More number of billionaires implies a higher level of taxes

Additionally, a global slicer for gender should be added to filter the analysis for each gender separately and there should be a chatbot to answer specific queries in the context of the dataset.

Step 2. Setting the expectations for the application

There are many variables associated with the application which can be customized as per the user’s vision. For example, this application is instructed to deploy proper navigation tools to switch between different views so that the charts are not cramped. Secondly, it is asked to incorporate a chatbot that can answer specific questions related to the dataset.

Apart from these, the user can adjust the color theme, spacing, headings, chart types, etc, just by instructing the builder.

Step 3. Creating the preview with a draft prompt

Though it is suggested to start with a small prompt and refine it on the go, for this guide a descriptive prompt is preferred keeping in mind the limitations over the number of prompts in the free version of Lovable.dev. It is suggested to refine the prompts with the help of tools like ChatGPT before feeding it to the AI-builder, to ensure the best results with a minimum number of prompts. For this demonstration, the following prompt has been used.

Take the CSV file of World’s Billionaire’s data as input and create an interactive dashboard. Use proper navigation tools to switch between different views so that the charts are not cramped.
At the beginning, there should be an option to upload the CSV file. The following elements should be included:

A chart to demonstrate if age is an important determinant of wealth.

A chart to answer if higher literacy implies a higher number of wealthy individuals.
A chart to give an idea about the top sources for wealthy individuals.

A chart to check if most of the people are self-made or not?

A global slicer for Gender.

A chart to analyze if the GDP of the country has anything to do with the literacy level

A chart to analyze if more number of billionaires implies a higher level of taxes

A chatbot that can answer specific questions related to the dataset


First page preview:

The page looks impressive, it has the upload button at the top, an AI assistant at the right, a dedicated plot area, and a set of navigation buttons. It also has the Gender slicer at the top left.

Step 4. Testing the page for its shortcomings

This step involves uploading the relevant data and testing it extensively for any shortcomings or scope for improvement. Some of the previews are displayed here for reference:

Step 5. Refining the prompt to handle the flaws

In this step, the application is tweaked based on the observations of the previous step. For example in the chart ‘Age vs Wealth’ the y-labels overlap with the y-ticks, so relevant instruction can be provided to have the desired spacing. Similarly, say if the Gender slicer does not work as expected, course correction can be done with the right prompt. If for a particular hypothesis, the user wants to change the chart type, all that is needed is to ask with proper reference and context.

Step 6. Deploying and utilizing the application for the set objectives

Publishing the page with Lovable.dev is a matter of one click. After publishing, updating the page is also as easy as publishing itself. Once deployed, the Lovable.dev application allows users to upload a dataset of billionaires and instantly generates interactive visualizations aligned with predefined analytical goals. Users can explore insights such as the relationship between age and wealth, education levels across countries, top sources of wealth, and economic indicators. With built-in filters and an intuitive layout, the application empowers users to derive meaningful conclusions without writing a single line of code.

Limitations of lovable.dev

While Lovable.dev offers a powerful and user-friendly interface for generating dashboards, it has a few limitations. Customization options may be limited compared to traditional BI tools like Power BI or Tableau, especially for highly specific visual or analytical needs. The platform also relies on descriptive prompts, so unclear instructions can lead to less accurate outputs. Additionally, advanced data transformations or multi-source data integration may require external preprocessing before upload.

Final Words

AI app builders are on the rise. With time they are expected to handle more complex, enterprise-level applications and also become more affordable. At present, they are not potent enough to completely replace tools like PowerBI but it won’t be a surprise if tomorrow it does. Dashboarding is one of the domains that has been demonstrated here, but these tools are going to positively impact every other domain likely be it e-commerce, content management systems, social networking, or game development.

References:

1. Lovable Documentation
2. Kaggle Dataset