AI as a Development Tool

Source: Dev.to

“…the greatest part of the questions and controversies that perplex mankind depending on the doubtful and uncertain use of words, or (which is the same) indetermined ideas…” An Essay Concerning Human Understanding, The epistle to the reader (1690)

Introduction

This year, many terms have emerged describing a new development tool – AI.
Immediately, as always, discussions began about the “taste” of different‑colored pencils: some believe they can derive pleasure from it, others think it’s only for production – and even then, only after the church’s blessing.

In principle, you can take any rule or best practice of development/engineering, replace the name of any tool with “AI,” and smack your opponents right on the head with them.

While willing experts are feeling up this elephant and arguing about what they’ve touched or felt, let’s try to establish some terminology.
We need something simple and understandable, so we can immediately point to where we are on the map.

I, too, can use smart words, so let’s try a paradigmatic approach.
From here on – no irony – let’s try to establish working definitions.

Goal of this document – not to evaluate AI as “good/bad,” but to provide a common language for discussion:

• what exactly we’re doing now,
• what level of risk we’re accepting, and
• which practices are appropriate for the chosen approach.

1. Vibe Coding

Definition – A developer describes desired functionality in human‑understandable language and generates code using AI without detailed review or editing of the result. Emphasis is on experimentation, rapid prototyping, and trust in AI rather than on manually writing or checking each line of code.

Intent → AI → Code

• AI writes code based on high‑level intent
• Minimal review, minimal structure
• Maximum speed, maximum risk

When to use

• Prototypes
• Demos
• Spikes
• One‑off code

When not to use

• Production systems
• Core logic
• Security‑sensitive paths

2. AI‑Assisted Development (AIAD)

Definition – Use of artificial‑intelligence tools to support the developer at various stages of development: writing code, testing, debugging, optimization, and automation of repetitive tasks. The developer remains an active participant; AI acts as an assistant that offers ideas, automates routine tasks, and improves code quality.

Human‑led development using AI as a tool
Human owns architecture and decisions
AI accelerates implementation
Standard reviews and testing apply

When to use

• Writing production features
• Maintaining long‑lived codebases

3. AI Pair‑Programming

Definition – AI assistants work alongside the developer in real time, suggesting alternatives, detecting errors, optimizing code, and even generating new features based on context. This reduces debugging time and improves project architecture.

Human ⇄ AI in real time
Continuous dialogue
AI suggests, human decides
Comparable to pair programming with a strong junior/mid developer

When to use

• Day‑to‑day development
• Learning unfamiliar codebases or languages

4. AI‑Generated Specifications & Architecture

Definition – AI is used for automatic generation of architectural decisions, diagrams, documentation, and test scenarios based on requirements. This allows quickly obtaining a system prototype and verifying its compliance with business requirements.

Requirements → AI → Specifications / Diagrams
AI helps before coding
Generates draft specifications, architecture diagrams, and test plans
Human reviews and refines

When to use

• System design
• Architecture exploration
• Early planning stages

5. AI‑Augmented Spec‑Driven Development

Definition – Structured specifications (requirements, architectural constraints, acceptance criteria, etc.) are the single source of truth for AI‑powered code generation. AI transforms these specs into implementation, tests, and documentation, ensuring high alignment, reducing error risk, and increasing speed.

Specification → AI → Code + Tests
Specification is the source of truth
AI generates code and tests based on specifications
Highest predictability and maintainability

When to use

• Core business logic
• Financial, security, or regulated systems
• Large codebases with long lifespans

6. Fully Integrated AI Development

Definition – AI is deeply integrated into all stages of development – design, code writing, testing, optimization, and even deployment. The developer and AI work as partners, significantly increasing productivity and code quality.

Human + AI jointly drive the process
AI participates in design, coding, testing, and optimization
Human remains responsible for strategy and constraints

When to use

• Teams intentionally adopting AI into the SDLC
• Clear constraints and evaluation processes exist

7. AI‑Driven Refactoring & Optimization

Definition – AI automatically analyzes and refactors existing code, identifying potential problems, suggesting optimizations, and adapting to project changes. It learns from large codebases to better understand context and requirements.

Existing code → AI → Improved code
AI refactors, optimizes, and reduces technical debt
Operates under strict rules and is subject to evaluation

When to use

• Controlled refactoring
• Performance optimization
• Style and consistency improvements

8. AI‑Powered CI/CD & Security Automation

Definition – AI can automatically generate CI/CD configurations, analyze code security, identify technical debt, and suggest remediation. This reduces risk and accelerates deployment.

Policy → AI → CI/CD and security automation
AI generates and maintains pipelines
Automates security scanning and policy enforcement

When to use

• DevSecOps automation
• Reducing operational risk

9. Autonomous AI Agents

Definition – AI agents can independently perform tasks – from generating code according to specifications to automatically creating tests, monitoring, and even fixing bugs in production. This enables self‑protecting systems that minimize human intervention.

Goal → AI Agents → Execution
Autonomous agents decompose and execute tasks
Minimal human intervention

Use with extreme caution

• Internal tools
• Clearly bounded responsibilities

Summary

Summary Table

Automated Development Paradigms

Decision Tree – What Are We Discussing?

Key Takeaway

AI doesn’t change engineering; it just makes the mistakes faster – or more manageable.

Runtime‑Signal‑Driven Self‑Healing (High Autonomy, High Risk)

Runtime signals → AI → Fix → Deploy

• AI monitors production, detects problems, generates patches, and automatically applies them.
• Highest autonomy, highest risk – not recommended by default.
• Use only with strict safeguards and only for non‑critical systems.