Generative AI in Developer Workflow: Beyond Autocompletion

Generative AI has moved beyond the gadget stage. It’s no longer just a tool that autocompletes for loops — it’s a set of tools that, when properly integrated, transform a senior developer’s productivity. But the key is “properly integrated”. Without method, AI generates mediocre code you’ll spend more time debugging than writing yourself.

Here’s how I integrate AI into my daily workflow — and where I draw the line.

AI as productivity tool, not replacement

Let’s kill the myth first: AI doesn’t replace senior developers. It replaces repetitive, low-value tasks. Architectural thinking, domain understanding, and technical judgment remain irreplaceable.

What AI does well:

• Generate boilerplate code from a clear spec
• Propose implementations of known patterns
• Write tests for existing code
• Rephrase and document complex code
• Detect problematic patterns in code review

What AI does poorly:

• Decide a system’s architecture
• Understand undocumented business constraints
• Evaluate long-term trade-offs
• Guarantee generated code security

Concrete use cases

Code generation

AI excels at structural code — DTOs, migrations, API routes, standard UI components. The time saving is real when context is clear.

Effective prompt:
"Create a Vue 3 composable useDebounce<T> that takes a ref and a delay,
returns a debounced ref. Use watchEffect and clearTimeout."

Ineffective prompt:
"Create a debounce hook"

Output quality directly depends on prompt quality. A senior who knows exactly what they want gets usable code in one iteration. A junior who prompts vaguely gets code that “looks correct” but breaks on edge cases.

AI-assisted code review

AI as a complementary reviewer catches things the human eye misses:

• Unused variables
• Missing error handling
• Naming inconsistencies
• Excessive cyclomatic complexity
• Common security vulnerabilities (SQL injection, XSS)

The workflow: AI reviews first, human reviews after. AI catches the mechanical, human judges the architectural.

Test generation

This might be the use case with the best ROI. AI generates unit and integration tests from existing code:

"Write Vitest tests for this validateOrder() function.
Cover: valid order, empty cart, negative amount, quantity 0,
non-existent product. Use describe/it, no external mocks."

AI generates 80% of test cases correctly. The developer adds business edge cases that only domain knowledge can identify.

For E2E tests, the approach is different — Playwright tests require understanding user journeys that AI can’t guess alone.

Documentation

Documenting legacy code is a chore. AI transforms it into a 5-minute task:

"Document this OrderProcessor class in JSDoc.
Explain the role of each public method, parameters, returns,
and possible exceptions. Concise and technical style."

The result is generally good for technical documentation. For architectural documentation (ADR, C4 diagrams), AI assists but doesn’t decide.

Integration into daily workflow

My typical workflow with AI:

1. Morning: PR review with AI assist for initial screening
2. Coding: AI for boilerplate, developer for business logic
3. Tests: AI generates the base, I complete business cases
4. Documentation: AI writes, I validate and enrich
5. Architecture: AI as sparring partner to explore options

AI integrates particularly well into a Product Management for developers approach: it accelerates execution and frees time for product thinking.

Tools in my stack:

• Claude Code: CLI for refactoring, migrations, codebase exploration
• Cursor: IDE for intensive development sessions with wide context

Limits and vigilance

Hallucinations

AI invents APIs, methods, parameters that don’t exist. Always verify against official documentation. This is particularly dangerous for:

• Package versions (AI mixes versions)
• Framework APIs (Symfony 7 vs 6, Next.js 14 vs 15)
• Environment-specific configurations

Security

AI-generated code can contain subtle vulnerabilities:

• SQL injection via concatenated queries
• XSS in dynamic rendering code
• Hardcoded secrets in examples
• Obsolete or vulnerable dependencies

Rule: all generated code goes through the same security review as human code. No free pass because “AI wrote it”.

Dependency and atrophy

The long-term risk: relying so heavily on AI that you lose the ability to solve complex problems without it. My safeguard: regular coding sessions without AI, katas, and active technical reading.

My 2026 AI setup

Tool	Usage	Frequency
Claude Code (CLI)	Refactoring, migrations, exploration	Daily
Cursor	IDE with wide context, intensive dev	Daily
Claude (web)	Research, brainstorming, documentation	Multiple times/day

Total cost: ~€80/month. Estimated ROI: 5-10 hours saved per week on mechanical tasks. This freed time goes into architectural thinking and product discovery.

In summary

Generative AI is a productivity multiplier for senior developers who know how to:

1. Prompt effectively with precise context
2. Systematically validate output (hallucinations, security)
3. Draw the line between assistance and dependency
4. Choose the right tools for each task

AI doesn’t make you a better developer. It frees up time for you to become one.