AI in Software Engineering: What It’s Good At, What It’s Not, and How to Use It Safely

Artificial intelligence has become unavoidable in software engineering. From code generation to documentation to infrastructure analysis, AI tools promise dramatic productivity gains. In practice, they deliver real value — but only when used with discipline.

This article outlines where AI actually helps, where it quietly introduces risk, and how experienced engineers can use it safely without surrendering judgment or control.

This perspective is based on real-world system design, maintenance, and failure analysis — not marketing claims.

## What AI Is Actually Good At

AI excels at tasks that are pattern-based, bounded, and reviewable.

### 1. Accelerating Familiar Work
AI is very effective when the engineer already understands the problem domain.
Examples:

- Drafting boilerplate code
- Translating logic between languages
- Generating test cases from known behavior
- Refactoring for readability
- Summarizing existing code or configs

In these cases, AI acts like a fast junior assistant — helpful, but not authoritative.

### 2. Drafting Documentation and Explanations
AI performs well at:

- Turning rough notes into structured documentation
- Explaining existing code paths
- Summarizing tickets, commits, or logs
- Creating first drafts of PRDs or build plans

The key distinction: AI is organizing known information, not discovering truth.

### 3. Navigating Large, Complex Systems
AI is useful for:

- Explaining terminology
- Connecting related concepts
- Pointing you toward relevant sections of documentation
- Translating vendor language into practical explanations

Used this way, AI reduces cognitive overhead — it does not replace verification.

## Where AI Quietly Fails

The most dangerous failures are not obvious errors. They are confident-sounding inaccuracies.

### 1. False Confidence and Hallucination
AI will produce:

- Plausible but incorrect APIs
- Non-existent configuration options
- Outdated behavior presented as current
- Internally inconsistent answers

These failures are especially dangerous because they often look reasonable and are difficult to detect without experience.

### 2. Context Loss in Real Systems
AI does not understand:

- Why a system evolved the way it did
- Historical constraints
- Business tradeoffs
- Operational risk
- Organizational reality

It may recommend technically “clean” solutions that are operationally disastrous.

### 3. Overstepping Into Decision-Making
AI cannot:

- Accept accountability
- Evaluate risk tolerance
- Understand regulatory exposure
- Balance uptime vs change velocity

Any workflow where AI is allowed to decide rather than assist is already unsafe.

## Why Terminal-Based AI Tools Matter

GUI-driven AI tools optimize for accessibility. Terminal-based tools optimize for control.

In professional environments, terminal-first workflows offer:

- Versioned prompts and outputs
- Scriptable, repeatable usage
- Integration with source control
- Explicit inputs and outputs
- Reduced ambiguity

This mirrors why engineers prefer:

- Git over drag-and-drop
- Infrastructure as code over click-based configuration
- Logs over dashboards alone

The medium enforces discipline.

## How to Use AI Safely in Software Engineering

### 1. Treat AI Output as Untrusted Input
AI output should be handled like user input or third-party data. It must be reviewed, tested, and validated.

### 2. Anchor AI With Documentation
AI performs better when grounded in:

- Clear PRDs
- Explicit build plans
- Known system architecture
- Existing tickets and decisions

Well-documented systems reduce hallucination risk by constraining ambiguity.

### 3. Keep Humans in the Loop — Always
AI can propose, draft, suggest, and summarize.
It should not approve, deploy, configure security, or define scope.
The moment AI output bypasses human judgment, risk escalates rapidly.

### 4. Use AI to Reduce Friction, Not Responsibility
AI should make you faster, clearer, and more consistent.
It should not make you less careful, less accountable, or less aware of system behavior.

## A Practical Rule of Thumb

If the cost of being wrong is:

- Low → AI is usually safe
- Moderate → AI must be reviewed carefully
- High → AI should only assist, never decide

Security, billing, data integrity, and infrastructure changes always fall into the high-cost category.

## Final Thoughts

AI is neither a replacement for engineers nor a gimmick to be dismissed. It is a powerful tool that rewards discipline and punishes complacency.

Teams that succeed with AI:

- Document aggressively
- Maintain clear ownership
- Understand their systems deeply
- Use AI as leverage, not authority

This is how we approach AI-assisted engineering at Peninsula Logic — with curiosity, caution, and respect for real-world consequences.