Building AI Features for a Hotel Booking App

In this project, we'll build a full stack AI-powered hotel booking platform using Python, Flask, React, and large language models (LLMs). We'll implement four practical AI features: intelligent hotel search, guest review analysis, AI-powered support ticketing, and automated booking confirmations. To support these features, we'll build a provider-agnostic LLM integration that works with Gemini, OpenAI, or Claude through a single configuration and transforms natural language input into reliable, structured, and application-ready output.

We'll start by creating reusable LLM utility functions for generating conversational responses across multiple AI providers. We'll then implement structured JSON outputs with predefined schemas, enabling the application to reliably extract hotel preferences, analyze guest sentiment, identify review issues, categorize support requests, and process booking information. We'll use Pydantic for data validation and SQLAlchemy for database operations, connecting LLM-generated data with real backend workflows.

By the end of this project, we'll have hands-on experience building a full stack generative AI application, integrating multiple LLM providers, validating structured AI outputs, and using them to power real-world hotel search, review analysis, customer support, and booking automation workflows.