AI Coding on the Engineering Track: Planning, Specifications, and Validation Practices

When “Writing More” Turns Into “Drifting Farther”

In recent years, giving natural-language requirements directly to AI coding agents and letting them quickly generate code has become widely discussed in developer communities. It is often described as intuition-driven coding, or “vibe coding.” The appeal is obvious: you barely need to design directory structures, interface contracts, or data models in advance. As long as you describe the idea, the tool can produce runnable code.

But this approach has a hidden cost: the faster code is produced, the faster the gap between project structure and real requirements tends to accumulate. During prototyping, this gap may not matter much, since the work will probably be rebuilt anyway. But once a project enters real development, coding without planning, rules, or traceable records can quickly turn the codebase into a high-entropy state that is hard to maintain, hard to regression-test, and hard to hand off. The more AI writes, the harder it becomes for the team to tell which code is reliable and which code needs to be rewritten.

In other words, the real engineering challenge is not “how to make AI write more code,” but “how to make AI write the right code under clear engineering constraints.”

Diagnosis: Why Coding Agents Go Off Track

Before bringing AI coding back under control, it is important to understand why coding agents drift. The main causes fall into four categories:

1. Context drift: An agent’s effective context window is limited. In long-running tasks, design decisions made early can gradually fall outside its attention range, causing later implementation to diverge from the original intent.
2. Runaway autonomy: If an agent is allowed to plan and refactor on its own at a large scale, it may keep expanding scope, introducing abstractions, dependencies, and features that were not requested. This leads to scope creep.
3. Lack of persistent memory: After each session, the AI does not inherently remember why previous design decisions were made. Without an externalized memory mechanism, consistency across sessions can only be maintained by human memory.
4. Missing validation: Without explicit validation checkpoints, AI tends to treat “generated” as “completed.” There is then no objective way to verify whether the generated code truly meets requirements or breaks existing functionality.

These four issues point to the same conclusion: AI coding needs an external, explicit engineering scaffold that constrains AI behavior within verifiable rails.

Methodology: Four Pillars for Bringing AI Coding Back on Track

1. Plan First, but Keep Planning in Human Hands

The core principle is “plan first, then code,” and the planner should be the human, not the AI. Developers should first define requirement boundaries, technology choices, module division, and acceptance criteria, then turn them into a reviewable plan. AI should only implement the plan item by item rather than deciding on its own what the project should look like. This fundamentally suppresses scope creep and gives every change a clear basis.

2. Emphasize Modularity to Control the Change Radius

A plan should be decomposed into modules, with each module having a single responsibility and clear boundaries. The value of modularity is that it limits the change radius of a single AI task: one module is changed at a time while the rest remains stable. When something goes wrong, it is easier to locate and roll back. This is much more robust than asking AI to generate the entire system in one pass, and it also makes code review practical.

3. Validate Every Step, with Human Confirmation

An ideal workflow is: “AI generates a small step → automatic or manual validation → proceed only after passing.” Validation can include unit tests, type checks, static analysis, and runnable smoke tests. The key is to treat “validation passed” as a hard gate for moving forward, not as an afterthought. Humans should remain in the loop and be responsible for confirming the output of each step.

4. Keep Traceable Documentation as Persistent Memory

Since AI does not have inherent cross-session memory, key decisions must be externalized into documentation. A common practice is to create a “memory bank” style directory that records requirements, technology-stack recommendations, implementation plans, and progress. At the beginning of a new session, let the AI read these documents first. This allows it to quickly restore a consistent working state instead of re-explaining context from scratch.

An example knowledge-base structure is as follows:

project/
├── docs/                   # Memory bank: requirements, plans, and progress
│   ├── requirements.md     # Requirements and acceptance criteria
│   ├── tech-stack.md       # Technology choices and rationale
│   ├── plan.md             # Step-by-step implementation plan
│   └── progress.md         # Progress and validation records for each step
├── src/                    # Application code
├── tests/                  # Validation and tests
└── prompts/                # Reusable system prompts

Putting It Into Practice: A Reusable Development Workflow

Combining these principles produces a clear workflow. Using a new repository from scratch as an example:

1. Create the memory bank: Before writing code, create the requirements document, technology-stack recommendation, implementation plan, and an empty progress record.
2. Align on the first step: Ask the AI to read the memory bank, implement only the first module in the plan, and clarify the acceptance criteria for that step.
3. Execute and validate: After the AI produces code, run tests and static checks, and have a human review whether the changes match expectations.
4. Record and proceed: Once validation passes, write the decisions, issues encountered, and results into the progress document, then move to the next step.
5. Iterate in cycles: Repeat “implement → validate → record” until the plan is complete. Any requirement outside the plan should first return to the planning stage for evaluation rather than being implemented directly by the AI.

The value of this workflow is that it turns AI from a casual code generator into an efficient implementer operating under clear constraints.

Prompt Engineering: Configurable Multi-Version Strategies

If documentation is the AI’s “long-term memory,” then the system prompt is its “working specification.” In practice, tasks of different complexity require different behavioral constraints. A pragmatic engineering approach is to make system prompts configurable and switchable across multiple versions. Common layering strategies include:

• Engineering-specification type: Constrains basic rules such as naming, directory structure, error handling, and prohibited behaviors. Suitable for most routine tasks.
• Deep-reasoning type: Requires the AI to analyze thoroughly and weigh options before writing code. Suitable for complex design decisions.
• Dedicated-planning type: Has the AI act as a planner first, producing only plans and breakdowns without writing code. An implementer then executes the plan. This role separation further reduces the risk of runaway autonomy.

An example system-prompt fragment is shown below. It expresses constraint intent only and is not tied to a specific tool’s syntax:

You are the coding assistant for this project. Follow these constraints:
1. Strictly execute the steps in docs/plan.md and complete only one module at a time.
2. Do not expand requirements or introduce dependencies outside the plan without approval.
3. After each step, output the self-validation method for that step and wait for human confirmation before continuing.
4. If you find a flaw in the plan, point it out and suggest changes first; do not unilaterally change scope.

The key is that prompts should be version-controlled along with the project and refined through iterations. They should become reusable team assets, not something written casually and discarded each time.

Organizing the Repository as a Knowledge Base

Finally, fixing documents, prompts, automation scripts, and other artifacts in agreed locations is the foundation for making this method effective over the long term. Treating the entire repository as a continuously iterable knowledge base brings three direct benefits:

• Discoverability: New team members or new sessions can quickly find requirements, plans, and specifications, reducing repeated communication.
• Iterability: Specifications and prompts accumulate as the project evolves, allowing team experience to be reused.
• Auditability: Decisions and validations at each step are recorded, making it possible to trace why things were done a certain way when issues arise.

Conclusion: Making AI an Executor Under Engineering Constraints

Intuition-driven coding is not inherently wrong. It greatly lowers the barrier from idea to prototype. But when the goal shifts from “fast validation” to “long-term maintainability,” the missing pieces must be filled in: planning, rules, and traceable documentation. Bringing planning authority back to humans, using modularity to limit change scope, using validation as a gate for progress, and using a memory bank to maintain cross-session consistency are not ways to limit AI’s capabilities. They provide a stable, verifiable engineering framework for those capabilities. Only then can AI truly move from “writing fast” to “writing correctly and maintainably.”