Matcha: Building a Local-First AI Resume–JD Matching Engine with Spring AI
Introduction Building an AI application as a backend developer no longer requires pivoting to a new language or managing complex cloud infrastructure. By leveraging Spring AI , you can treat a Large Large Model (LLM) as just another service in your ecosystem. Matcha was prototyped and polished in just 3–4 hours . This speed is possible because Spring AI abstracts the "AI complexity" into familiar POJO-based patterns, allowing for rapid iterations—tuning prompts and refining logic in minutes rather than days. To ensure a systematic engineering defense of the architecture, I applied the S.C.A.L.E. Framework . This framework turns the chaos of open-ended design into a structured, defensible plan by focusing on trade-offs rather than just components. S: Scope and Size Let's begin by defining the Requirements (The MVP) and then calculating the Constraints . This defines the project boundary for a local-first recruitment tool. Functional Requirement (FR): The user can upload ...