Why 90% of Developers Misuse Codex for Spring Boot – The Critical First Mistake

Recently I tested many AI coding tools such as Codex, Claude Code, and Cursor. I initially focused on which model is stronger, which tool generates code faster, which agent fixes bugs better, and which understands Spring Boot best.

After repeated experiments I realized that poor results are not due to weak models but to the low‑quality tasks given to the AI. Developers often open a Spring Boot project and issue a single natural‑language request like “implement order refund functionality” or “optimize this module”. The AI then rewrites packages, return structures, exception handling, transaction boundaries, and even modifies unrelated public components. The developer inspects the changes and concludes “AI still doesn’t work”.

In reality the failure stems from missing engineering context: the AI was never told the project’s task description, development constraints, coding standards, or acceptance criteria. A real development task contains many concrete items – refund interface location, order‑status design, payment callback handling, idempotency, inventory rollback, MQ usage, testing requirements, immutable modules, return‑structure conventions, etc. Most developers omit this information and assume the AI can infer everything, which leads to the core problem.

AI coding in production requires a clear “engineering task sheet” rather than a vague wish. The sheet must describe background, goals, scope of changes, allowed files, prohibited modifications, project conventions, and verification steps. Providing such a sheet dramatically changes Codex’s output.

For example, a one‑sentence prompt “add a refund query interface to the order system” yields a generic controller, service, DTO, and even guessed table structures. In contrast, a detailed prompt that specifies the module, existing return type Result<T>, exception class BusinessException, XML‑based MyBatis mappers, allowed directories, and forbidden changes produces a focused, project‑compliant implementation.

A good task sheet also includes a “do not modify” section, which prevents the AI from unintentionally touching common modules, database migration scripts, production configuration, or performing risky operations. This restriction is crucial because the AI tends to “complete” missing information on its own.

Many teams lack an AGENTS.md (or CLAUDE.md, CODEX.md) file that records project‑level rules for the AI. Unlike a README aimed at humans, this file contains technical details such as module layout, where new interfaces belong, unified return structures, exception handling policies, pagination format, mapper style, test commands, and directories that must not be altered.

The article provides a concrete AGENTS.md template for a Spring Boot project, listing the tech stack, module descriptions, coding conventions, test requirements, prohibited actions, and Codex output expectations. It also shows a full‑task‑sheet template with sections for background, objectives, allowed and forbidden scopes, project conventions, reference implementations, verification, and output requirements.

Using such structured task specifications reduces AI “self‑initiative” errors and makes the generated code reviewable, testable, and merge‑ready. The author argues that the emerging key skill for Java engineers will be the ability to decompose complex requirements into clear AI‑compatible task sheets, rather than merely writing clever one‑line prompts.

In summary, the gap between successful and failed AI‑assisted development is not the model itself but the quality of the task description. Providing a detailed engineering task sheet enables AI to respect project conventions and deliver reliable code.