Building a Production-Grade Spring Boot Application from Scratch Using a Large Language Model (LLM)

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In today’s fast-paced software development ecosystem, the ability to build reliable, maintainable, and scalable applications efficiently is paramount. Combining the power of Spring Boot, a production-ready Java framework, with the capabilities of large language models (LLMs) such as ChatGPT or GitHub Copilot, developers can significantly accelerate their development process while maintaining high standards in code quality and architecture.

This article provides a comprehensive, step-by-step guide on how to build a production-grade Spring Boot application using an LLM as your AI coding assistant.

Define the Project Requirements

Before diving into code, use the LLM to help brainstorm and clarify the project scope. Let’s assume we want to build a simple Task Management API with the following features:

  • Create, update, delete, and list tasks

  • Use a PostgreSQL database

  • Expose REST endpoints

  • Use Docker for deployment

  • Apply security best practices (e.g., JWT)

  • Provide integration tests

You can ask your LLM:

“Can you help me define a minimal set of microservices and architecture patterns for a task manager application using Spring Boot?”

This results in a helpful conversation to establish:

  • Domain-driven design

  • Layered architecture

  • Database schema

  • Technologies like Spring Data JPA, Spring Security, JWT, etc.

Bootstrap the Project

You can use Spring Initializr manually, or ask the LLM:

“Generate a Spring Boot pom.xml file for a RESTful Task API with PostgreSQL, Spring Security, and Lombok.”

Example pom.xml:

xml
<dependencies>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-web</artifactId>
</dependency>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-data-jpa</artifactId>
</dependency>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-security</artifactId>
</dependency>
<dependency>
<groupId>org.postgresql</groupId>
<artifactId>postgresql</artifactId>
</dependency>
<dependency>
<groupId>io.jsonwebtoken</groupId>
<artifactId>jjwt</artifactId>
<version>0.9.1</version>
</dependency>
<dependency>
<groupId>org.projectlombok</groupId>
<artifactId>lombok</artifactId>
<scope>provided</scope>
</dependency>
</dependencies>

LLMs can also help you understand version compatibility and suggest the correct plugins.

Define the Domain Model

Ask the LLM:

“Can you define a Task entity with fields: id, title, description, dueDate, and status (enum)? Use Lombok and JPA annotations.”

Task.java:

java
@Entity
@Data
@NoArgsConstructor
@AllArgsConstructor
public class Task {
@Id
@GeneratedValue(strategy = GenerationType.IDENTITY)
private Long id;private String title;
private String description;
private LocalDate dueDate;@Enumerated(EnumType.STRING)
private Status status;public enum Status {
PENDING, IN_PROGRESS, COMPLETED
}
}

LLMs can also refactor the code into different layers—DTOs, services, and mappers—on request.

Build Repository and Service Layers

LLMs can help you generate JPA repositories and services using standard interfaces.

TaskRepository.java:

java
public interface TaskRepository extends JpaRepository<Task, Long> {
List<Task> findByStatus(Task.Status status);
}

TaskService.java:

java
@Service
@RequiredArgsConstructor
public class TaskService {
private final TaskRepository taskRepository;public Task createTask(Task task) {
return taskRepository.save(task);
}public List<Task> getAllTasks() {
return taskRepository.findAll();
}public void deleteTask(Long id) {
taskRepository.deleteById(id);
}
}

Prompt example:

“Generate a TaskServiceImpl with create, read, update, delete operations using a TaskRepository.”

Create REST Controllers

Ask the LLM:

“Create a TaskController exposing CRUD operations mapped to /api/tasks.”

TaskController.java:

java
@RestController
@RequestMapping("/api/tasks")
@RequiredArgsConstructor
public class TaskController {private final TaskService taskService;@PostMapping
public ResponseEntity<Task> createTask(@RequestBody Task task) {
return new ResponseEntity<>(taskService.createTask(task), HttpStatus.CREATED);
}@GetMapping
public List<Task> getAllTasks() {
return taskService.getAllTasks();
}@DeleteMapping(“/{id}”)
public ResponseEntity<Void> deleteTask(@PathVariable Long id) {
taskService.deleteTask(id);
return ResponseEntity.noContent().build();
}
}

Add Security with JWT

Ask your LLM:

“How do I add stateless JWT-based security to a Spring Boot application?”

The LLM can help generate:

  • User model and repository

  • JWT Utility class

  • AuthController

  • SecurityConfig using UsernamePasswordAuthenticationFilter

SecurityConfig.java (simplified):

java
@EnableWebSecurity
public class SecurityConfig extends WebSecurityConfigurerAdapter {@Override
protected void configure(HttpSecurity http) throws Exception {
http
.csrf().disable()
.authorizeRequests()
.antMatchers(“/api/auth/**”).permitAll()
.anyRequest().authenticated()
.and()
.sessionManagement().sessionCreationPolicy(SessionCreationPolicy.STATELESS);
}
}

Write Integration Tests

Prompt:

“Write a Spring Boot test for TaskController using MockMvc to test task creation.”

TaskControllerTest.java:

java
@SpringBootTest
@AutoConfigureMockMvc
public class TaskControllerTest {@Autowired
private MockMvc mockMvc;@Test
public void testCreateTask() throws Exception {
String taskJson = “{\”title\”:\”Test Task\”,\”description\”:\”Unit test\”,\”dueDate\”:\”2025-06-01\”,\”status\”:\”PENDING\”}”;mockMvc.perform(post(“/api/tasks”)
.contentType(MediaType.APPLICATION_JSON)
.content(taskJson))
.andExpect(status().isCreated());
}
}

Add Docker Support

Prompt:

“Create a Dockerfile and docker-compose.yml to run Spring Boot with PostgreSQL.”

Dockerfile:

dockerfile
FROM openjdk:17
COPY target/task-api.jar app.jar
ENTRYPOINT ["java", "-jar", "/app.jar"]

docker-compose.yml:

yaml
version: '3.8'
services:
app:
build: .
ports:
- "8080:8080"
depends_on:
- db
environment:
SPRING_DATASOURCE_URL: jdbc:postgresql://db:5432/tasks
SPRING_DATASOURCE_USERNAME: user
SPRING_DATASOURCE_PASSWORD: passdb:
image: postgres:14
environment:
POSTGRES_DB: tasks
POSTGRES_USER: user
POSTGRES_PASSWORD: pass

Add Monitoring and Observability

Prompt:

“How can I add Actuator and Prometheus to monitor a Spring Boot app?”

Add to pom.xml:

xml
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-actuator</artifactId>
</dependency>

Then configure application.yml:

yaml
management:
endpoints:
web:
exposure:
include: "*"
metrics:
export:
prometheus:
enabled: true

CI/CD Pipeline (Optional)

Prompt:

“Create a GitHub Actions workflow for building and testing a Spring Boot app.”

.github/workflows/build.yml:

yaml

name: Build and Test

on: [push, pull_request]

jobs:
build:
runs-on: ubuntu-latest
steps:
– uses: actions/checkout@v2
– name: Set up JDK 17
uses: actions/setup-java@v3
with:
java-version: ’17’
distribution: ‘temurin’
– name: Build with Maven
run: mvn clean install

How LLMs Accelerate the Process

Using an LLM as your assistant offers:

  • Rapid code generation

  • Instant answers to Java or Spring questions

  • Fast bug fixes and refactoring suggestions

  • Improved consistency in naming, architecture, and patterns

  • Learning on-the-go for junior developers

A developer can go from zero to a fully running production-grade application within hours instead of days.

Conclusion

Building a production-grade Spring Boot application from scratch has traditionally involved deep architectural planning, substantial boilerplate coding, and careful attention to testing, security, and deployment. These requirements, while critical for scalable and reliable systems, often slow down the development cycle and can become a bottleneck, especially for small teams or solo developers.

Enter Large Language Models (LLMs) like ChatGPT, Claude, and GitHub Copilot. These AI assistants fundamentally reshape how we approach software engineering by acting as real-time collaborators—ones that never sleep, have vast knowledge repositories, and can generate or refactor code based on natural language instructions.

By combining the power of Spring Boot—a mature and well-supported framework for building microservices and enterprise Java applications—with the contextual intelligence of LLMs, developers can now achieve a level of productivity and architectural rigor previously limited to large engineering teams.

Throughout this article, we demonstrated how LLMs can:

  • Clarify and define application requirements early in the planning phase

  • Rapidly bootstrap Spring Boot projects, including pom.xml generation, dependency management, and starter code

  • Assist in designing robust domain models with JPA and Lombok annotations

  • Generate and validate layered architectures with proper separation of concerns

  • Accelerate the creation of REST APIs, security configurations, and integration tests

  • Provide consistent help in setting up containerized deployments via Docker and Docker Compose

  • Guide developers through monitoring, logging, and CI/CD pipeline integration

Moreover, LLMs significantly reduce context switching, a common issue in full-stack development, where developers often need to jump between Java code, SQL schemas, YAML files, frontend configuration, and cloud setup scripts. With an LLM as an assistant, you can remain in your IDE while asking questions like, “How do I expose an actuator health endpoint?” or “How can I secure this endpoint with JWT and roles?”

Even more importantly, LLMs encourage best practices and modern architectural decisions. For example, when asking an LLM to generate a service layer or an API controller, you are often given results that align with contemporary standards such as DTO usage, error handling patterns (e.g., global exception handling), and test-driven development.

However, it’s essential to note that LLMs amplify developer productivity—they do not replace architectural understanding. You still need to review generated code, validate security implementations, ensure code consistency, and run performance profiling to tune your production setup. LLMs provide the scaffolding, but the responsibility of creating a truly scalable and maintainable system remains with the development team.

In conclusion, using a large language model to help build a Spring Boot application represents the next evolution of intelligent software development. It empowers developers to shift focus from repetitive tasks to strategic decision-making and high-level design. The fusion of Spring Boot’s production-readiness with LLM-driven development creates a powerful, modern workflow that fosters rapid innovation, robust implementation, and operational excellence.

Whether you’re a solo developer building a SaaS MVP or part of a larger team looking to streamline your engineering pipeline, this approach unlocks new efficiencies and capabilities. By leveraging these tools today, you’re future-proofing your development process and embracing a new era where AI-assisted software engineering becomes the norm, not the exception.