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Behind the Scenes: How We Built the AI-Powered Restaurant App

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Welcome to Day 7 of 12 Days of Exploring AI! Over the past few days, I've shown you the app that personalizes restaurant recommendations based on your preferences. But how does it actually work? Today, I'll take you behind the scenes and explain the magic—powered by Large Language Models (LLMs).

The Problem: Static Search Results

Traditional tools like Google Maps or Yelp give you a list of restaurants based on basic filters like "Italian near me." But they don't know you:

  • What are you looking for today? Coziness? Fast service? Great vegetarian options?
  • Why does this restaurant suit you perfectly?

We solved this by building an app that:

  • Understands your preferences.
  • Analyzes restaurant data and reviews.
  • Matches your input to the best option—and explains why.

Here's how it works, step by step.

Step 1: Collecting the Data

The foundation of this app is data, and we fetch it from the Google Maps API:

  • Place data: Information like the name, location, and category of a restaurant.
  • Review data: All the customer reviews we can get our hands on.

Step 2: Processing the Data with LLMs

Once we have the data, we need to make sense of it. This is where the power of LLMs comes into play:

Summarizing the Data

  • The LLM processes the raw restaurant information and reviews.
  • It summarizes what the restaurant is about in plain, human-like language.
  • Example: "Cozy Italian spot with great vegetarian options and quick service."

Extracting Key Points

From the reviews, we extract structured insights like:

  • Coziness: "warm and inviting atmosphere"
  • Service speed: "quick and efficient service"
  • Special options: "extensive vegetarian menu"

Step 3: Embeddings—Making Text Searchable

Here's where it gets technical (and magical):

  • We create embeddings—numerical representations of the meaning behind the restaurant descriptions and reviews.
  • This allows us to "compress" the meaning of all that unstructured data into a format that can be easily searched.
  • All these embeddings, along with the summaries, are stored in our database.

The real power here comes from OpenAI's embedding model, which gives us direct access to the core understanding capabilities of their LLMs (as we explored on Day 1). This technology lets us tap into the same semantic understanding that makes ChatGPT so powerful, but specifically for matching and comparing text.

Step 4: Matching Your Preferences

When you type in your preferences—something like "I want a cozy Italian restaurant with good vegetarian options":

Creating Your Embedding

  • The LLM takes your input and creates an embedding that represents the meaning of your request.

Finding the Best Match

  • We search our database for the restaurant whose embedding most closely matches your input.
  • This step connects your preferences to reviews and restaurant data in a way that feels natural and human-like.

Evaluating the Match

  • To give it a human touch, we feed your preferences and the selected restaurant back into the LLM.
  • The LLM evaluates the match and generates an explanation:

    "You're looking for coziness and vegetarian options. This restaurant scores 9/10 for coziness and has great vegetarian dishes—it's a perfect fit!"

Why This Is Powerful

The magic here comes from the ability of LLMs to understand language on different dimensions:

  • They connect what you say (your preferences) with what others say (the reviews), even if the phrasing is completely different.
  • They transform messy, unstructured data into clear, actionable insights.

This is personalization done like a pro—powered by AI but still feeling human.

It's Just the Beginning

Of course, this is still a prototype. The matching isn't yet perfect, and fine-tuning will be needed to get everything spot on. But even in this early stage, the possibilities are thrilling.

Why? Because this app gives me exactly what I need—a way to cut through the noise and find what matters most to me. I tend to forget restaurant names, but I care about the details, and this tool lets me skip the search and go straight to the best option.

What's Next? AI Agents

This restaurant app is just one piece of the puzzle. The next few days, we'll explore something even bigger: the world of AI Agents. These systems can work autonomously to perform tasks and make decisions, and they have the potential to replace much of today's SaaS tools and human labor.

Exciting? Absolutely. A little scary? Maybe. But one thing's for sure: the future is here.

Want to Explore AI in Your Business?

How could these techniques—data summarization, embeddings, and personalization—apply to your business? Do you have unstructured data that's waiting to be turned into insights?

👉 Let's explore what's possible together!

Tomorrow we dive into the Agents use case for restaurants. Do you know of someone already busy creating that?