Let’s illustrate this with an example.

Imagine an application managing patient records, which allows for custom user-defined fields. Unlike standard fields, such as patient name or ID, users can define additional fields based on specific calculations they require. This flexibility ensures our application can cater to diverse medical scenarios without needing code modifications.

For instance, consider a doctor who wants to calculate the Body Mass Index (BMI) and display it on the report of his patient. The application doesn’t have provision to capture this information. The doctor can introduce a new field without taking help from support, and he can define a formula that will derive value for this field. 

This is where all the magic happens. The doctor doesn’t need to write formula on his own, he can just AI to write it for him!!

User Input:

“BMI of the patient”

Our code prepends and appends some boilerplate code to make it a look like a prompt which looks like following,

Final Input sent to the model:

Calculate the BMI of a patient considering the following data of a patient,
{ “ID”: “1”, “Name”: “Vaishakhi”, “HeightInCms”: “165”, “WeightInPounds”: “132” }
Output should be a return statement. Do not include comments in the output.

The JSON model is the representation of a patient’s information that is already present in our database.

Output from the model: The AI model analyzes the input and generates the corresponding javascript expression for the intended functionality. Output looks similar to this,

const bmi = weight / (height * height);
return bmi;