/how-to-build-lovable

How to build Language learning app with Lovable?

Step by step guide to build a language learning app with Lovable, covering UX design, development, content strategy and launch tips to boost retention

Matt Graham, CEO of Rapid Developers

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How to build Language learning app with Lovable?

Yes — you can build a chat-first language-learning app entirely inside Lovable by using Chat Mode to create the React frontend (chat UI, lesson pages), storing Supabase credentials in Lovable Cloud Secrets for user/progress storage, and using Preview to iterate. For anything requiring CLI (custom server deployments, advanced Supabase SQL or Edge Functions), export/sync to GitHub from Lovable and run the needed commands locally or in CI; otherwise no terminal is required.

 

What we’re building / changing

 

We’ll create a small chat-first language learning app in Lovable with:

  • Chat UI for practice
  • Lessons list and user progress saved in Supabase
  • Secrets stored in Lovable Cloud (SUPABASE_URL, SUPABASE_ANON\_KEY)
  • Previewable in Lovable and publishable via Lovable Publish or GitHub export

 

Lovable-native approach

 

Use Chat Mode edits to create/modify files (no terminal). Add Supabase credentials via Lovable Cloud Secrets UI, then use Preview to test the app. If you need server-side SQL/policies or Edge Functions, use GitHub export/sync and perform those steps outside Lovable (clearly labeled).

 

Meta-prompts to paste into Lovable Chat Mode

 

Paste each prompt into Lovable’s chat to have Lovable implement the change. Replace placeholders (like SUPABASE\_URL) with your real values via Lovable Secrets UI.

 

Prompt 1 — Scaffold frontend files and basic routes

// Goal: Create core frontend files: App, Home, Lesson, Chat component and Supabase client
// Files to create/modify:
// - src/App.tsx -> set up router and basic navigation
// - src/pages/Home.tsx -> lesson list UI
// - src/pages/Lesson.tsx -> lesson detail and chat area
// - src/components/Chat.tsx -> chat-first practice UI
// - src/lib/supabase.ts -> create supabase client reading from environment
// Acceptance criteria:
// - App renders Home at / and Lesson at /lesson/:id
// - Chat component shows input and message list
// - supabase client imports process.env.REACT_APP_SUPABASE_URL and REACT_APP_SUPABASE_ANON_KEY

 

Prompt 2 — Add simple Supabase usage for saving progress

// Goal: Add saveProgress and loadProgress functions using the Supabase client
// Files to modify:
// - src/lib/supabase.ts -> export saveProgress(userId, lessonId, progress) and loadProgress(userId)
// - src/pages/Lesson.tsx -> call saveProgress when user finishes an exercise (simulate via "Mark complete" button)
// Acceptance criteria:
// - saveProgress inserts/updates a progress record (client-side call)
// - Lesson page shows saved progress after saving
// - No secrets are hard-coded; client reads environment variables

 

Prompt 3 — Wire Secrets and instructions for Lovable UI

// Goal: Add clear instruction for user to add Lovable Cloud Secrets
// Files to modify:
// - README.md -> add steps: Open Lovable Cloud Secrets UI and add SUPABASE_URL and SUPABASE_ANON_KEY
// Acceptance criteria:
// - README contains exact secret names: SUPABASE_URL, SUPABASE_ANON_KEY
// - Preview will fail gracefully if secrets missing, showing a helpful message in the UI

 

Prompt 4 — Add Preview checks and test data

// Goal: Add a Demo mode when SUPABASE_* secrets are not set to allow Preview testing
// Files to modify:
// - src/lib/supabase.ts -> export isDemo if secrets missing and provide in-memory fallback functions
// - src/pages/Home.tsx -> show a "Demo mode" banner when using demo
// Acceptance criteria:
// - Preview works without secrets in demo mode
// - Chat interactions function using in-memory state

 

Secrets / integration setup steps

 

  • Open Lovable Cloud Secrets UI and add: SUPABASE_URL and SUPABASE_ANON\_KEY (names must match code)
  • Supabase Dashboard (outside Lovable): create project, table "progress" with columns user_id, lesson_id, progress (if you want persistent storage). If you need Row-Level Security or SQL setup, export to GitHub and perform those steps in the Supabase Dashboard/CLI.

 

How to verify in Lovable Preview

 

  • Open Preview: load / and confirm Home shows sample lessons
  • Open a lesson: interact with Chat, type messages, click "Mark complete"
  • With secrets set: verify saving loads back progress; without secrets, app runs in demo mode

 

How to Publish / re-publish

 

  • Use Lovable Publish button in the UI to deploy the app directly from Lovable
  • If you need server-side SQL, Edge Functions, or advanced deployment: export/sync to GitHub from Lovable, then run CLI tools locally (this is outside Lovable and requires terminal)

 

Common pitfalls in Lovable (and how to avoid them)

 

  • Missing Secrets: always add SUPABASE_URL and SUPABASE_ANON\_KEY in Lovable Secrets UI; otherwise use demo fallback
  • Exposing keys: never commit keys to repo; use Lovable Secrets only
  • Expecting a terminal: you cannot run migrations or create Supabase policies inside Lovable — export to GitHub and do those outside
  • CORS or RLS issues: if saving fails, check Supabase RLS policies in the Supabase Dashboard (outside Lovable)

 

Validity bar

 

  • Works fully inside Lovable: frontend, chat UI, secret injection, Preview, Publish
  • Requires external work: creating Supabase tables, RLS policies, Edge Functions — do via GitHub export and Supabase dashboard/CLI

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How to add a per-user SRS (SM-2) backend to a Lovable language app

This prompt helps an AI assistant understand your setup and guide to build the feature

AI AI Prompt

How to add per-user practice streaks in Lovable

This prompt helps an AI assistant understand your setup and guide to build the feature

AI AI Prompt

How to add practice session checkpoints

This prompt helps an AI assistant understand your setup and guide to build the feature

AI AI Prompt

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!

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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 Language learning app with AI Code Generators

You should build a conversation-first app with modular prompt templates, use Lovable’s Chat Mode to iterate UI + prompts, store secrets in Lovable Cloud Secrets, persist user and progress data in Supabase, keep server-side logic in small serverless endpoints (for grading, lesson generation, and analytics), and rely on Lovable Preview + GitHub sync/Publish to validate production behavior without a terminal. Prioritize safety, cost-control (rate-limits, token budgets), and clear instrumentation so you can improve prompts from real usage.

 

Design conversation-first UX

 

Keep interactions as single-turn or small-step multi-turn exchanges so the model and your app state are predictable.

  • Chunk activities into focused tasks (e.g., vocabulary quiz, sentence correction, pronunciation prompt) rather than a giant “teach me” request.
  • Save minimal state (lesson id, user level, recent turns) server-side so you can recreate context safely.

 

Prompt templates & evaluation

 

Separate prompts from code and version them. Use deterministic evaluation rubrics for grading so results are stable.

  • Template parts: system instruction, user instruction, example pairs, and scoring rubric.
  • Keep templates small and feed only the context needed to reduce token cost.

 

Data & personalization with Supabase

 

Use Supabase for auth, user profiles, progress, and analytics. Store prompts, user answers, and model feedback for iterative improvement.

  • Use row-level security to protect user data.
  • Persist evaluations so you can retrain prompts or produce teacher reports.

 

Secrets & environment variables in Lovable Cloud

 

Put API keys and DB URLs in Lovable Secrets (not in code). Refer to them as process.env.VAR\_NAME in server code. Rotate keys and use scoped service keys for server APIs.

  • Set OPENAI_API_KEY, SUPABASE_URL, SUPABASE_SERVICE\_KEY in Lovable Secrets UI.
  • Avoid exposing service keys to the frontend—use server endpoints as a proxy.

 

Lovable-native development workflow

 

Use Chat Mode edits and file diffs to iterate code and prompts; run feature checks in Preview; publish to GitHub when you need CI / package installs. You cannot run a terminal inside Lovable, so avoid instructions that require running npm locally inside the app environment.

  • Iterate prompts and UI in Chat Mode — make small diffs and use Preview to exercise endpoints.
  • Use Publish/GitHub sync when you must add packages or run CI pipelines externally.

 

Production considerations

 

Enforce rate limits, implement caching for repeated prompts, instrument token usage, and provide human review paths for low-confidence evaluations.

  • Rate-limit endpoints by user and session to control cost.
  • Log inputs/outputs (with consent) for prompt tuning and safety audits.

 

// pages/api/grade.js  (Next.js-style serverless endpoint)
// Uses OpenAI chat completion and stores result to Supabase via REST
export default async function handler(req, res) {
  // Get secrets from environment (set these in Lovable Secrets UI)
  const OPENAI_KEY = process.env.OPENAI_API_KEY;
  const SUPABASE_URL = process.env.SUPABASE_URL;
  const SUPABASE_SERVICE_KEY = process.env.SUPABASE_SERVICE_KEY;

  // // Validate input
  const { userId, lessonId, userAnswer } = req.body;
  if (!userId || !lessonId || !userAnswer) return res.status(400).json({ error: 'missing' });

  // // Prompt template (kept short)
  const messages = [
    { role: 'system', content: 'You are a strict language tutor. Grade the student answer 0-100 and give a short correction.' },
    { role: 'user', content: `Student answer: "${userAnswer}"\nTask: translate to Spanish or correct grammar. Output JSON: {score: int, feedback: string}` }
  ];

  // // Call OpenAI
  const openaiResp = await fetch('https://api.openai.com/v1/chat/completions', {
    method: 'POST',
    headers: { 'Content-Type': 'application/json', 'Authorization': `Bearer ${OPENAI_KEY}` },
    body: JSON.stringify({ model: 'gpt-3.5-turbo', messages, max_tokens: 300 })
  });
  const openaiData = await openaiResp.json();
  const text = openaiData.choices?.[0]?.message?.content || '';

  // // Try to parse JSON safely
  let result = { score: null, feedback: text.trim() };
  try {
    const parsed = JSON.parse(text);
    result.score = parsed.score ?? null;
    result.feedback = parsed.feedback ?? text.trim();
  } catch (e) {
    // // fallback: keep raw text as feedback
  }

  // // Store evaluation in Supabase (REST)
  await fetch(`${SUPABASE_URL}/rest/v1/evaluations`, {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
      // // use service role key for server-side writes
      'apikey': SUPABASE_SERVICE_KEY,
      'Authorization': `Bearer ${SUPABASE_SERVICE_KEY}`
    },
    body: JSON.stringify({ user_id: userId, lesson_id: lessonId, answer: userAnswer, score: result.score, feedback: result.feedback })
  });

  return res.json({ ok: true, score: result.score, feedback: result.feedback });
}

 

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