/how-to-build-lovable

How to build Personalization system with Lovable?

Learn how to build a personalization system with Lovable. This step-by-step guide shows you practical strategies for creating tailored user experiences that drive engagement.

Book a free  consultation
Matt Graham, CEO of Rapid Developers

Book a call with an Expert

Starting a new venture? Need to upgrade your web app? RapidDev builds application with your growth in mind.

Book a free No-Code consultation

How to build Personalization system with Lovable?

 
Understanding the Lovable Personalization System
 

  • This guide demonstrates how to build a personalization system using Lovable within your project. Lovable offers prebuilt personalization logic that you can integrate by including its dependencies and configuration directly in your source code.
  • You will need a Lovable account with your API key, a working project file (for example, an HTML/JS web application), and the ability to add code snippets that simulate dependency installation since Lovable has no terminal.

 
Setting Up Dependency Inclusion in Code
 

  • Since Lovable does not have a terminal to run package installation commands, you must include its library directly in your code by referencing the CDN.
  • Open your HTML file (for example, index.html) where your application loads. Inside the <head> section, add the following snippet to load Lovable:
    • 
<script src="https://cdn.lovable.com/lovable.min.js"></script>
  • This snippet acts as your dependency installer by referencing the Lovable library online.

 
Creating the Lovable Configuration File
 

  • Create a new JavaScript file named lovable-config.js in your project’s root directory. This file will store all configuration parameters for your personalization system.
  • Add the following snippet inside lovable-config.js. Replace YOUR_LOVABLE_API\_KEY with your actual Lovable API key and customize any personalization rules as needed:
    • 
// lovable-config.js


      const lovableConfig = {
        apiKey: "YOUR_LOVABLE_API_KEY",
        personalizationRules: [
          {
            condition: "visitorAge < 30",
            personalize: {
              theme: "youthful",
              headline: "Discover what's trending for you!"
            }
          },
          {
            condition: "visitorLocation === 'US'",
            personalize: {
              theme: "modern",
              headline: "Welcome to our US portal!"
            }
          }
          // Add additional rules as needed
        ]
      };
      

      if (typeof module !== "undefined") {
        module.exports = lovableConfig;
      }



  • This file provides the necessary settings and personalization conditions that Lovable will use to adapt your application.

 
Integrating Lovable into Your Application
 

  • In your main JavaScript file (for example, app.js or main.js), you need to initialize Lovable using the configuration defined earlier.
  • If your project structure supports modules, include the configuration by adding the following code at the top of your main file. If not, you can simply paste the configuration code directly in your main file.
    • 
// At the beginning of your main JS file (e.g., app.js)
  

      // If using modules:
      const lovableConfig = require('./lovable-config.js');
      

      // If modules are not supported, copy the content of lovable-config.js here.
      

      // Initialize Lovable personalization system
      const lovable = new Lovable(lovableConfig);
      

      // Function to evaluate and apply personalization for a visitor
      function personalizeVisitor(visitorData) {
        const rules = lovableConfig.personalizationRules;
        for (let rule of rules) {
          // A simple evaluation example; in a real scenario, use proper parsing or a rules engine.
          if (eval(rule.condition.replace("visitor", "visitorData"))) {
            applyPersonalization(rule.personalize);
            return;
          }
        }
        // Default personalization if no rules match
        applyPersonalization({ theme: "default", headline: "Welcome to our site!" });
      }
      

      // Example function to update the UI with personalization settings
      function applyPersonalization(settings) {
        document.body.setAttribute('data-theme', settings.theme);
        const headlineElement = document.getElementById("headline");
        if (headlineElement) {
          headlineElement.textContent = settings.headline;
        }
      }



  • This snippet shows how to include the Lovable configuration, initialize the Lovable object, and define a function to personalize the visitor experience.

 
Implementing Personalization Event Triggers
 

  • Determine when to execute personalization logic. For example, you might want to personalize the UI on page load based on visitor data.
  • Below is a snippet you can place at the end of your main JS file to run personalization once the document is ready. This example assumes visitor data is available (e.g., via a visitor object).
    • 
// Example visitor data (replace with real data retrieval)
const visitorData = {
  visitorAge: 25,
  visitorLocation: "US"
};


      // Run personalization when the page is fully loaded
      document.addEventListener('DOMContentLoaded', function() {
        personalizeVisitor(visitorData);
      });



  • This integration ensures that as soon as your webpage loads, visitor information is processed and the appropriate personalized settings are applied.

 
Validating Your Integration and Testing the System
 

  • Review your code to ensure that the Lovable script reference is correctly placed in your HTML and the corresponding JavaScript files are linked.
  • Check that lovable-config.js contains your API key and customization rules, and that it is properly imported or its content is present in your main file.
  • Test your application by simulating different visitor data scenarios. Modify the visitorData object values manually to verify that different personalization scenarios trigger the correct changes on the page.
  • Adjust the personalization rules in lovable-config.js as necessary to fine-tune the user experience.

 
Finalizing and Deploying Your Personalization System
 

  • Once you have confirmed that the personalization logic works as expected in your development environment, save all modified files.
  • Deploy your project by following your usual deployment steps. With Lovable integrated directly into your code, any future updates to personalization rules will only require modifying lovable-config.js or the related logic in your main JS file.
  • Regularly review visitor data and adapt your personalization rules in Lovable for continuous improvement of user experience.

Want to explore opportunities to work with us?

Connect with our team to unlock the full potential of no-code solutions with a no-commitment consultation!

Book a Free Consultation

How to Build a Personalized API with Lovable


"use strict";
const express = require('express');
const bodyParser = require('body-parser');
const Lovable = require('lovable-personalization');

const app = express();
app.use(bodyParser.json());

const lovableEngine = new Lovable({
  apiKey: process.env.LOVABLE_API_KEY,
  config: { weightStrategy: 'frequency' }
});

const userPreferences = {
  "user\_123": { "sports": 3, "music": 5, "tech": 2 },
  "user\_456": { "sports": 1, "music": 4, "tech": 5 }
};

app.post('/api/personalize', async (req, res) => {
  const { userId, contentItems } = req.body;
  if (!userId || !Array.isArray(contentItems)) {
    return res.status(400).json({ error: 'Invalid input data.' });
  }
  
  try {
    const userProfile = userPreferences[userId] || {};
    const structuredItems = contentItems.map(item => {
      const interestWeight = userProfile[item.category] || 0;
      return { ...item, score: Math.random() + interestWeight };
    });

    const personalizedList = await lovableEngine.rank(structuredItems);
    res.json({ userId, personalizedList });
  } catch (err) {
    res.status(500).json({ error: 'Error processing personalization.' });
  }
});

const PORT = process.env.PORT || 3000;
app.listen(PORT, () => {
  console.log(`Personalization API listening on port ${PORT}.`);
});

How to Build an Advanced Personalization API with Lovable


"use strict";
const express = require('express');
const axios = require('axios');
const Lovable = require('lovable-personalization');

const app = express();
app.use(express.json());

const lovableEngine = new Lovable({
  apiKey: process.env.LOVABLE_API_KEY,
  config: { weightStrategy: 'recency' }
});

// Simulated in-memory store for user preferences
const userPreferences = {
  "user\_A": { "tech": 3, "fashion": 2, "sports": 1 },
  "user\_B": { "tech": 1, "fashion": 4, "sports": 3 }
};

app.post('/api/advanced-personalize', async (req, res) => {
  const { userId, contentItems } = req.body;
  if (!userId || !Array.isArray(contentItems)) {
    return res.status(400).json({ error: 'Missing or invalid userId/contentItems.' });
  }

  try {
    // Fetch additional engagement data from an external analytics API
    const analyticsResponse = await axios.get(`https://external-analytics.example.com/engagement/${userId}`);
    const engagementData = analyticsResponse.data; // e.g., { tech: 0.8, fashion: 1.2, sports: 0.5 }
    
    // Merge stored preferences with external engagement score via weighted average
    const userStoredPrefs = userPreferences[userId] || {};
    const mergedProfile = {};
    for (const key of Object.keys(engagementData)) {
      const storedScore = userStoredPrefs[key] || 0;
      // Weighted average: 60% external, 40% stored
      mergedProfile[key] = (engagementData[key] _ 0.6) + (storedScore _ 0.4);
    }
    
    // Enrich each content item with computed score from merged profile based on its category tag.
    const enrichedItems = contentItems.map(item => {
      const categoryScore = mergedProfile[item.category] || 0;
      return { ...item, score: categoryScore + Math.random() \* 0.2 };
    });
    
    // Rank items using Lovable personalization engine.
    const personalizedList = await lovableEngine.rank(enrichedItems);
    res.json({ userId, personalizedList });
  } catch (error) {
    console.error('Error processing personalization:', error);
    res.status(500).json({ error: 'Failed to personalize content due to external API issues.' });
  }
});

const PORT = process.env.PORT || 4000;
app.listen(PORT, () => {
  console.log(`Advanced Personalization API running on port ${PORT}`);
});

How to Build a Personalization System with Lovable in Your Node.js App


const express = require('express');
const { ApolloServer, gql } = require('apollo-server-express');
const Lovable = require('lovable-personalization');

const app = express();

const lovableEngine = new Lovable({
  apiKey: process.env.LOVABLE_API_KEY,
  config: { weightStrategy: 'time\_decay' }
});

const userBehavior = {
  "user1": { tech: 2, health: 1, entertainment: 3 },
  "user2": { tech: 5, health: 3, entertainment: 1 }
};

const trendingContent = [
  { id: 'a', category: 'tech', title: 'Tech Trends 2023' },
  { id: 'b', category: 'health', title: 'Healthy Living Tips' },
  { id: 'c', category: 'entertainment', title: 'Blockbuster Movie Releases' },
  { id: 'd', category: 'tech', title: 'Gadget Innovations' }
];

const typeDefs = gql\`
  type Content {
    id: String
    category: String
    title: String
    score: Float
  }
  type Query {
    personalizedFeed(userId: String!): [Content]
  }
\`;

const resolvers = {
  Query: {
    personalizedFeed: async (\_, { userId }) => {
      const userPrefs = userBehavior[userId] || {};
      const itemsWithScores = trendingContent.map(item => {
        const baseScore = userPrefs[item.category] || 0;
        return { ...item, score: baseScore + Math.random() \* 0.5 };
      });
      const rankedItems = await lovableEngine.rank(itemsWithScores);
      return rankedItems;
    }
  }
};

const server = new ApolloServer({ typeDefs, resolvers });
server.start().then(() => {
  server.applyMiddleware({ app });
  const PORT = process.env.PORT || 5000;
  app.listen(PORT, () => console.log(`GraphQL Personalization API running on port ${PORT}`));
});

Want to explore opportunities to work with us?

Connect with our team to unlock the full potential of no-code solutions with a no-commitment consultation!

Book a Free Consultation
Matt Graham, CEO of Rapid Developers

Book a call with an Expert

Starting a new venture? Need to upgrade your web app? RapidDev builds application with your growth in mind.

Book a free No-Code consultation

Best Practices for Building a Personalization system with AI Code Generators

 

Understanding AI Code Generators and Personalization

 

  • This guide explains how to build a personalization system by leveraging AI code generators. The system tailors code or content to individual users based on their profiles, preferences, and behaviors.
  • It is designed for a non-tech audience, breaking down complex processes into accessible, step-by-step instructions.
  • Before starting, understand that AI code generators are tools or APIs that convert descriptive inputs into code snippets, using machine learning models to generate context-specific outputs.

 

Defining Your Goals and Requirements

 

  • Identify what aspects of user experience you want to personalize (e.g., code snippets, layout adjustments, feature recommendations).
  • Determine key performance indicators (KPIs) such as improved user engagement or reduced development time.
  • Document the requirements in simple terms that outline how the personalization will benefit users.

 

Preparing Your Data

 

  • Collect relevant data such as user profiles, preferences, and behavior history. This data will inform the AI model on how to personalize its output.
  • Ensure that data collection complies with privacy regulations and that you have a secure storage plan.
  • For example, you might gather data in a simple JSON format:
    • 
{
  "name": "Alice",
  "role": "developer",
  "preferences": {
    "theme": "dark",
    "features": ["autocomplete", "code formatting"]
  }
}

 

Designing User Profiles and Behavior Patterns

 

  • Create simple user profiles that capture key details affecting personalization. Think of these as user "snapshots" that the system can quickly reference.
  • Decide on attributes like preferred programming language, experience level, and frequently used frameworks.
  • Define behavior patterns such as most accessed code features or typical coding errors to help the system predict needs.

 

Selecting and Integrating an AI Code Generator

 

  • Research available AI code generators that best suit your requirements. Compare features, costs, reliability, and ease of integration.
  • Choose one that provides a simple API accessible via HTTP requests. Make sure it supports customization based on user input.
  • Integrate the chosen tool into your system by setting up the API call. An example in Python might look like this:
    • 
import requests


      def generate_personalized_code(user_profile):
          url = "https://api.example-codegen.com/generate"
          payload = {
              "user_data": user_profile,
              "preferences": {"theme": user_profile.get("preferences", {}).get("theme", "light")}
          }
          response = requests.post(url, json=payload)
          return response.json()
      

      Example user profile
      

      user = {
          "name": "Alice",
          "role": "developer",
          "preferences": {"theme": "dark", "features": ["autocomplete", "code formatting"]}
      }
      

      code_output = generate_personalized_code(user)
      print(code_output)



  • Test different input parameters to see how the code generator adapts the generated output.

 

Setting Up a Feedback Loop for Continuous Improvement

 

  • Implement mechanisms for users to give feedback on generated code. This could be as simple as a thumbs-up/down system or detailed comments.
  • Collect and analyze feedback to refine personalization rules. Adjust user profiles and API parameters based on what works best.
  • Ensure that the feedback process is intuitive to encourage active user participation.

 

Testing and Tuning the Personalization System

 

  • Conduct usability tests with a small group of users to identify any issues with personalization.
  • Run A/B tests comparing personalized outputs against standard ones to determine effectiveness.
  • Adjust the configuration of your AI code generator and data collection methods based on test outcomes.
  • Verify that the system handles different data inputs gracefully, ensuring robustness and accuracy.

 

Client trust and success are our top priorities

When it comes to serving you, we sweat the little things. That’s why our work makes a big impact.

Rapid Dev was an exceptional project management organization and the best development collaborators I've had the pleasure of working with. They do complex work on extremely fast timelines and effectively manage the testing and pre-launch process to deliver the best possible product. I'm extremely impressed with their execution ability.

CPO, Praction - Arkady Sokolov

May 2, 2023

Working with Matt was comparable to having another co-founder on the team, but without the commitment or cost. He has a strategic mindset and willing to change the scope of the project in real time based on the needs of the client. A true strategic thought partner!

Co-Founder, Arc - Donald Muir

Dec 27, 2022

Rapid Dev are 10/10, excellent communicators - the best I've ever encountered in the tech dev space. They always go the extra mile, they genuinely care, they respond quickly, they're flexible, adaptable and their enthusiasm is amazing.

Co-CEO, Grantify - Mat Westergreen-Thorne

Oct 15, 2022

Rapid Dev is an excellent developer for no-code and low-code solutions.
We’ve had great success since launching the platform in November 2023. In a few months, we’ve gained over 1,000 new active users. We’ve also secured several dozen bookings on the platform and seen about 70% new user month-over-month growth since the launch.

Co-Founder, Church Real Estate Marketplace - Emmanuel Brown

May 1, 2024 

Matt’s dedication to executing our vision and his commitment to the project deadline were impressive. 
This was such a specific project, and Matt really delivered. We worked with a really fast turnaround, and he always delivered. The site was a perfect prop for us!

Production Manager, Media Production Company - Samantha Fekete

Sep 23, 2022