In this project, we’ll build a reusable LLM utility layer that provides a common interface for structured and unstructured LLM outputs. Modern applications often need more than conversational text; they also need structured output that can be validated before application logic consumes it. We’ll use Python and Flask to create a service that abstracts provider-specific LLM integrations. It will support Gemini, OpenAI, and Claude behind a common interface, with the provider selected through configuration, so application code can switch providers without adding provider-specific logic.
We’ll start by building shared utility functions to call different LLM providers and generate unstructured text responses from user prompts. We’ll then extend the utility layer to generate structured JSON responses that follow a predefined schema, making LLM output easier to validate and consume in application logic. Using hotel search and guest review scenarios, we’ll compare conversational responses with structured outputs and examine when unstructured text or structured output is the better fit. By the end of this project, we’ll have hands-on experience designing a reusable LLM integration layer that supports multiple providers and transforming model responses into validated data that application features can consume.