How to build an AI language tutor for your business with v0 (no coding required)

Rule of thumb: if you want to learn a language yourself, do not build this. Any row above teaches you more for less than the weeks below cost, and a curriculum written by people beats a generated deck for a beginner. If you teach, run a school, train staff or want a product of your own, the seat is the bill, and a tutor on your own key turns a price per learner into cents per lesson. The honest middle case is a tutor for beginners in a language you do not speak yourself, and there the right answer is to build it and have a native speaker check what the model generates before learners see it.

* On this template the free-tier line to watch is the pause rather than storage. Nothing large is stored, because conversations are not saved and no files are kept, so a learner is a few rows of text. What bites is that a free project sleeps after a week without activity, and a tutor people open when they have ten spare minutes is exactly the sort that goes quiet. Pro, from $25/mo, ends the pause and keeps a daily backup for 7 days.

Prices and rates from supabase.com, developex.com, v0.app, v0.app and vercel.com, checked September 2026.

The database step is the one worth not skipping once your screens need real data. Everything else here takes a couple of minutes, and after that you’re just describing what you want.

1. 01

   Generate the dashboard before anything else exists

   Start with the screen learners live in. Placeholder replies and cards are fine, and finding out the layout is wrong now costs nothing.

   PromptSketch the dashboardCopy

   Build the learning dashboard for a language tutor: a language picker at the top and four tabs. Chat Practice with a conversation and a text box, where the tutor’s reply shows a correction of the learner’s message beneath it. Flashcards that flip to reveal a meaning with a speaker button. Quick Exercises with a fill-in-the-blank sentence and an answer box. Dictionary with a word lookup and a translate box with one tap to swap the two languages. Add four streak counters above the tabs and a separate streak history page with a week view and a chart. Use placeholder content for now. I will add the real tutor next.

   Placeholder data is fine here, and only here. Everything after this step gets something real behind it.

2. 02

   Add the real tutor to the screen you just built, with the ceiling

   This is the step that turns a convincing mock-up into a tutor. The log and the limit go in with the first reply, because every turn resends the conversation and a long test is a bill.

   PromptBuild the tutor route and the ceilingCopy

   Add a server route that calls my AI provider with the key held in server environment variables, never in the browser. It takes the learner’s message, their target and native languages and the last ten turns, and asks the model to reply in the target language at the learner’s level and then, in the native language, to point out up to three mistakes and how to fix them without rewriting the whole message. Before it contacts the provider it writes a row to a calls log and checks the learner’s calls today against a limit read from settings, returning a friendly limit message when it is hit. Wire the Chat tab to it and render the reply as formatted text. Use a placeholder learner id for now.

   Be explicit that the key lives on the server. v0 will otherwise reasonably reach for a client-side call, since the screen already exists and it is the shortest path.

3. 03

   Start a new chat for the database and its routes

   Saving progress is a separate feature from practising, so give it its own chat. Restoring a version later then undoes the persistence work without taking the dashboard with it.

   PromptConnect a database and design the modelCopy

   In this chat, connect a Supabase database and create the tables and the API routes behind them. A profile per learner with their native and target languages. Activity completions with a type and the learner’s local day. Streaks per activity with the current and longest run. The calls log and the settings table from step 02. Make the dashboard read and write real data through the routes rather than placeholder content.

   Ask for the routes alongside the tables. A schema with nothing reading it leaves the dashboard exactly as fake as it was.

4. 04

   Add auth, checked on the server and in the database

   Two layers, same rule. A route check is the first line, row-level security is the second, and it is the one that still holds when a route forgets to filter.

   PromptAdd auth and access rulesCopy

   Add authentication. Email-and-password sign-up, login, logout, a session and a profile row per learner, with signed-out visitors sent to the login screen. Every API route, including the tutor route and the ones that only record activity, must check that a learner is signed in server-side rather than trusting anything the browser sends, and the tutor route must use the real learner id for the ceiling. Then turn on row-level security for every table so the database enforces the same thing: a learner reaches only their own rows, and every insert and update carries an explicit check that the row is not being filed under someone else.

   v0 routes run on the server, so the check belongs there. A limit enforced only in the component is a limit anyone can skip.

5. 05

   Add the drills and the dictionary, then tidy the dashboard with Design mode

   Four more routes behind the tabs you already have, all through the same ceiling. Spend the time you save on a proper visual pass over the dashboard.

   PromptAdd the drills and the dictionaryCopy

   In a new chat, add four more server routes, each through the same key, the same sign-in check and the same per-learner ceiling as the tutor: one that generates a set of vocabulary flashcards for the target language and a chosen difficulty, one that returns a fill-in-the-blank sentence at a chosen difficulty with its answer, one that defines a word, and one that translates a phrase between the two languages. Wire them to their tabs: cards that flip and read the word aloud with the browser’s own speech synthesis, exercises checked as the learner types, and the dictionary with the swap button working.

   For visual fixes on the dashboard, select the element in the preview and use Design mode rather than describing where it sits. It is faster and it changes the real code.

6. 06
   Destination

   Add streaks, settings and demo mode, then open a pull request

   The pieces around the lesson come last, then walk the whole thing through and merge, which, if that branch is the linked Vercel project’s production branch, also deploys it.

   PromptAdd streaks, settings, demo, and shipCopy

   Add a route that records a completed activity and updates that activity’s streak using the learner’s local day (consecutive days extend it, a missed day resets it, the longest run is kept), wire the counters and the history page to real data, and add a settings page with the learner’s languages, a password change and a usage panel showing calls and estimated cost this period. Add a demo mode where a visitor without an account sees the dashboard shell with sample content and no route accepts a call from them. Then help me walk the whole flow: open the demo signed out and confirm nothing calls the provider, sign up, hold a ten-turn conversation with mistakes in it, drill cards and an exercise, check the streaks moved, hit the daily limit on purpose and read what a learner sees. Once it holds up, publish and open a pull request for it.

Review the access rules (row-level security policies) on every table. For each one, tell me in simple terms who can view, add, edit, and delete records, confirm that people can only reach their own data while the right roles can reach more, and flag anything left open that shouldn’t be.

One rule outranks everything above it: your database service key and your AI provider key belong on the server only, never in the app a learner downloads and never in a repository. If either escapes, treat it as compromised, check your provider’s usage page for what was spent with it, and replace it the same day.

Choose on two things, neither of them speed. First, whether the plan permits commercial use, because Vercel’s Hobby tier does not and a tutor with paying learners is a business. Second, whether a bookmarked link to the streak page opens the app for a learner arriving cold, because their own progress is the page they bookmark. The tutor itself never touches the host: every reply comes from your database’s functions, and the host serves one small static folder.

Add a calls table that records every model call (which learner, which activity, which model, how many tokens went in and came back, whether it succeeded, and a cost estimate) and write to it from every function that calls the provider, successful or not. Then add a ceiling: a limit per learner per day and a total across the whole app, both settings I can change without a deploy, checked inside the function before it contacts the provider and returning a friendly "you have reached today’s practice limit" rather than an error. Do not let the client send the limit or the cost, because both are read on the server. Then add a small usage panel in settings showing calls and estimated spend for the current period.

Add a microphone button to the chat and the exercises that uses the browser’s speech recognition to turn what the learner says in the target language into text, shows the transcript for them to correct before it is sent, and falls back to the text box in browsers that do not support recognition for that language. Then extend the tutor prompt so that when a message arrived by voice, the reply also comments briefly on any word the transcript suggests was mispronounced, without turning the lesson into a pronunciation drill.

Add a five-exchange placement conversation for a new learner in their target language, send the transcript to the ai function and ask for a level on a simple scale with a one-line reason, store it on the profile, and use it as the default difficulty for flashcards and exercises. Re-evaluate every twenty completed activities and tell the learner when their level changes, letting them override it.

Save every generated flashcard and exercise to the learner’s account with whether they got it right, then add a "Review" tab that schedules cards with spaced repetition: the ones they missed come back tomorrow, the ones they knew come back in a week, then a month. Ask the ai function to generate a fresh example sentence for each card on its review day so the same word is met in a new context, and show the learner how many cards are due today on the dashboard.

v0 doesn’t ship its own model for these prompts, so you bring an API key for whichever provider you want, and v0 writes the route that calls it. Start with a cheap model while you’re still iterating on the prompt itself, then swap in a stronger one for the version that ships, and keep every AI feature behind that one route so there’s only one key to rotate.