Build an AI App with Any LLM: Lovable + OpenRouter Tutorial

Description

Learn how to build a powerful app that integrates with any LLM using Lovable and OpenRouter! In this step-by-step overview, I’ll show you how to set up seamless LLM integration, add secure payments with Stripe, and implement user authentication—all in one go.


0:00-0:16 Introduction and overview of an AI application built with Lovable.
0:16-0:45 Discussion of landing page design and how prompts can achieve great front-end styles.
0:46-1:04 User authentication setup using Superbase.
1:04-1:18 Superbase Integration to create a user account.
1:18-1:55 How to use Edge functions.
1:55-2:06 Overview of logging in and token purchasing.
2:06-2:47 Discussion on integrating Stripe payment processing to purchase tokens.
3:16-3:59 Open Router API framework is discussed in depth.
3:59-4:18 Overview of the code and other features of the application.
4:18-4:45 Prompt Studio
5:02-5:40 Using OpenAI, it can be tested to run prompts together.
7:16-8:24 Quick conclusion and additional insights!

Summary

This comprehensive tutorial demonstrates how to build a modern AI application using Lovable.dev and OpenRouter, showcasing the seamless integration of multiple large language models (LLMs) into a single application. The video walks through creating a full-featured web application with a professionally animated landing page, user authentication via Supabase, and Stripe payment processing for token-based usage. A key highlight is the integration with OpenRouter, which provides unified API access to multiple LLM providers through a single interface. The presenter demonstrates practical features including a prompt studio for testing different AI models simultaneously, comparing their responses, and storing reusable prompts. The application includes sophisticated features like streaming responses, custom Edge functions for payment processing, and a structured framework for prompt engineering. The tutorial emphasizes how Lovable.dev simplifies complex technical implementations, making it possible to build production-ready AI applications without extensive development experience. Particularly noteworthy is the application's ability to evaluate responses from different AI models using GPT-4, Claude, and other leading LLMs to determine optimal performance for specific use cases. The final product, available at prompt.withpair.com, demonstrates how modern development tools can streamline the creation of sophisticated AI applications with professional features like user management, payment processing, and multi-model AI integration.

Frequently Asked Questions

How can I quickly build and deploy an AI application without a large development team?

Lovable.dev provides an all-in-one solution for building AI applications with integrated authentication, payment processing, and LLM capabilities. Its low-code approach and pre-built components allow you to deploy production-ready applications in days rather than months, while its Supabase integration handles all backend requirements automatically.

What's the fastest way to integrate multiple AI models into a single application?

Lovable.dev's OpenRouter integration allows you to connect with multiple AI models through a single API, eliminating the need for separate implementations. This feature enables immediate access to models from OpenAI, Anthropic, and others, while Lovable.dev handles all the authentication and routing complexity behind the scenes.

How can I implement user authentication and payment processing in my AI application without writing complex backend code?

Lovable.dev offers built-in integration with Supabase for user authentication and Stripe for payment processing, requiring minimal setup. The platform's Edge functions handle all the complex backend logic for payment webhooks and user management, allowing you to focus on building your application's unique features.

What's the most efficient way to test and compare different AI models for my application?

Lovable.dev includes a built-in prompt studio that allows you to test multiple AI models simultaneously and compare their responses. This feature helps you identify the best-performing model for your specific use case, while the platform's structured prompt framework ensures consistent results across different models.

Transcript

0:01 all right so I wanted to talk through a 0:04 quick app that I made with lovable um 0:07 just to kind of go through some of the 0:08 different things that you can do um and 0:11 a lot of this was kind of surprising to 0:13 me because I think even a couple months 0:15 ago you could do a lot of this so here's 0:17 just a simple landing page um uh I can 0:21 go through a different video of how I 0:22 created kind of these animated uh 0:24 components um but I was pretty happy 0:27 with how those came out um and it takes 0:29 a while to kind of get the right styling 0:33 or design but once you kind of figure 0:35 out um how to how to prompt lovable you 0:38 can get some uh pretty great front end 0:41 designs I would say uh better than 0:43 cursor or some of the other tools I've 0:45 used all right 0:47 so uh this also has user off fully set 0:51 up I can log in with Google uh or just 0:56 a any email so let's just showcase that 1:01 um and basically to set up user off at 1:03 least as far as I'm aware you do have to 1:06 uh integrate with uh super base um so uh 1:10 it's it's not too hard to do um it's 1:13 pretty self-explanatory but you just 1:15 have to create a superbase account and 1:17 connect it uh and I can show you what 1:20 that looks like over here so here's the 1:22 the uh super based project um and Su Bas 1:26 is basically just like a way to kind of 1:28 simplify managing the the back end so 1:31 all your tables will be here which is 1:33 pretty cool um you can manage them you 1:36 can do insertions you can run Uh custom 1:40 SQL queries to make 1:41 changes um and I found it to be I don't 1:45 know I'm I'm definitely not super 1:47 technical and doing things through super 1:49 Bas is is uh has been really easy um uh 1:53 all 1:54 right so I've set up off and again can 1:57 do more videos going into more depth on 1:59 on this stuff the other thing that I've 2:01 sent set up is the ability to um 2:04 basically purchase uh tokens um and I've 2:07 set that up with a stripe 2:09 integration um and that's just via the 2:12 subbase edge function so to connect with 2:15 stripe uh at least I found you need to 2:17 you need a um uh a checkout session Edge 2:21 function and then a web hook to know 2:23 when that checkout session is 2:24 successfully completed um so basically 2:28 the first one will connect you to that 2:30 stripe checkout um to to enable people 2:33 to check out and then you have to you 2:34 know input your uh stripe uh API keys 2:38 and secrets and then the web hook will 2:41 be something that stripe sends to us to 2:43 tell us that someone successfully uh 2:46 checked out so that we know um to give 2:48 them credit for that so in this case the 2:50 the purchase is to to buy tokens so when 2:52 they buy a certain amount of tokens 2:54 there is a function that will tell us 2:56 that they made that purchase um great so 3:00 the other thing to I wanted to show off 3:01 is uh through Edge functions um have 3:05 been able to integrate uh something 3:07 called open router so open router is 3:09 kind of like a central API for as far as 3:11 I can tell all the models that you can 3:14 access via API so these are just four 3:16 that I've chosen but essentially uh you 3:19 can actually just go to the open 3:21 router website um you can pretty much 3:25 integrate with any of the models that 3:27 they have available here um which seem 3:30 to be uh pretty 3:32 extensive um they also have like 3:34 different rankings uh and this was just 3:37 really cool because just one API so I 3:39 can uh show that here so that's the the 3:42 stream uh Edge function um and this they 3:46 also support pretty much all the the 3:49 things that like the open AI API does so 3:52 streaming audio um web search uh I found 3:56 it to be really cool so I can also show 3:58 you what that looks like 4:00 and the 4:02 code we can go over here to 4:05 functions stream chat 4:12 um that's the um method to integrate the 4:16 different models uh the other thing 4:18 which I wanted to Showcase uh using that 4:20 is um the ability to uh run prompts 4:25 against uh a couple different models at 4:28 the same time um so let me just pick a 4:31 good uh one let's see well let's look at 4:34 the prompt Library uh product one 4:38 pager no that's just a placeholder 4:41 another placeholder okay 4:45 um so I'll use something that I actually 4:47 used in one of my other projects we'll 4:49 go here let's do Json converter so I was 4:52 looking for I want to prompt uh for an 4:56 llm that will convert um uh text into a 5:00 Json for something else that I'm working 5:01 on um and I wasn't sure who would create 5:04 the uh best prompt for that so with this 5:08 tool I can actually just select the 5:10 different models I want to test it 5:11 against and it will run the same prompt 5:14 uh across those different selected 5:24 models but something I like to do is 5:26 have uh one 5:30 model evaluate all the responses and 5:33 then help you figure out uh 5:36 which which model is actually doing the 5:39 best job so off screen I'm just copy 5:42 pasting these 5:45 responses now we're going to copy the 5:47 three prompts and 5:51 then I've been finding myself using 5:53 grock for a lot of things I don't know 5:55 why but it just seems pretty 5:56 well-rounded so I'll say here 6:02 are three 6:05 prompts or a 6:09 Json for a text to 6:13 Json 6:15 converter to 6:17 create 6:19 diagram Json 6:23 G pick the best one and explain why 6:30 all right copy paste all that and then 6:32 let's see what grock 6:40 thinks so now it's evaluating the the 6:43 three prompts I haven't told it which 6:45 models uh did which and let's see let's 6:48 see if it has a favorite um so gr chose 6:52 the third promp which was written by 6:56 chat GPT 6:57 4.5 so while it was a slow I have found 7:00 that 4.5 tends to win out in these uh 7:04 prompt evals um so worth considering 7:07 especially if you're thinking about you 7:09 know a prompt that you're going to use 7:11 uh often um 4.5 might be the best model 7:15 but anyways that's a quick overview of 7:16 this tool you can uh chat with different 7:19 models all from one chat um you don't 7:22 have to deal with different 7:23 subscriptions and then you also have the 7:26 prompt studio uh The Prompt creator um 7:30 which is helps you uh have a more 7:32 structured framework for creating promps 7:34 so you have um sort of these basic 7:37 sections which I found to be useful and 7:39 then a prompt library to kind of store 7:41 all your prompts as you're building them 7:43 out uh and then you can also um uh call 7:47 those prompts as a uh variable here 7:50 let's just do this one 7:56 because so you can see how that's 7:58 working but um yeah it took a while to 8:01 kind of figure it out but now that it's 8:03 set up um it's pretty reliable uh and 8:07 it's 8:09 available uh what's the actual URL I 8:11 think it's just available publicly yeah 8:14 prompt withth pair.com um so check it 8:17 out uh definitely still some bugs um 8:20 some issues with like the routing where 8:21 it like defaults back to the chat page 8:23 but um overall yeah pretty fun projects