What should developers consider when choosing AI tools?

AI Tools Are Amazing (And Also Terrible)

You've probably tried GitHub Copilot, ChatGPT, or one of the dozen other AI coding tools. Sometimes they blow your mind with perfectly generated code. Other times they confidently suggest code that doesn't work and would have been faster to write yourself.

The hype says AI will replace developers. The reality is messier: AI tools are incredibly useful for some things and completely unreliable for others. After using them in real projects, here's what actually matters when choosing AI tools for development.

What AI Tools Actually Do Well

AI tools excel at the boring, repetitive stuff that makes coding tedious:

• Generating boilerplate code and configuration files
• Writing tests for straightforward functions
• Explaining code you didn't write (or wrote but forgot)
• Transforming data between formats
• Suggesting API calls and library usage
• Helping with syntax in languages you don't use often

What AI Tools Are Terrible At

AI tools struggle with anything that requires understanding context or making architectural decisions:

• Understanding your specific business requirements
• Making architectural decisions that affect multiple systems
• Debugging complex issues that span multiple services
• Optimizing performance for your specific use case
• Understanding security implications of code changes
• Knowing when to break existing patterns

The Five Questions to Ask Before Choosing an AI Tool

1. What Problem Are You Actually Trying to Solve?

Are you spending too much time on boilerplate? Need help learning new APIs? Want faster code reviews? Different tools solve different problems. Don't pick a tool because it's popular—pick it because it solves your actual problem.

2. How Much Context Does It Need?

Some tools work great with isolated code snippets. Others need to understand your entire codebase. Tools that need more context often give better suggestions but are harder to set up and slower to use.

3. What Does It Do with Your Code?

Read the privacy policy. Some tools use your code to train their models. Others keep it private. If you're working on proprietary code, this matters. A lot.

4. How Does It Fail?

All AI tools fail sometimes. The question is how they fail. Do they obviously break? Do they subtly introduce bugs? Do they fail silently? Understanding failure modes helps you know when to trust the tool.

5. How Much Are You Willing to Spend?

Some tools are free, some are expensive. Factor in setup time, learning curve, and ongoing costs. A $20/month tool that saves you 5 hours a week is cheaper than a free tool that wastes your time.

The AI Tool Categories That Actually Matter

Code Completion Tools

Good for: Reducing typing, suggesting APIs, generating boilerplate

Examples: GitHub Copilot, Tabnine, CodeT5

Watch out for: Overreliance on suggestions, accepting code you don't understand

Code Review Tools

Good for: Catching obvious bugs, suggesting improvements, enforcing style

Examples: DeepCode, SonarQube with AI features

Watch out for: False positives, missing complex issues

Documentation Tools

Good for: Generating docstrings, explaining legacy code, creating README files

Examples: GitHub Copilot, Mintlify, Stenography

Watch out for: Generic documentation that doesn't explain business context

Debugging Tools

Good for: Suggesting fixes for error messages, explaining stack traces

Examples: ChatGPT, Claude, specialized debugging assistants

Watch out for: Confident wrong answers, not understanding your specific setup

How to Actually Evaluate AI Tools

Start Small

Don't commit to expensive tools immediately. Most have free trials or free tiers. Try them on real work, not demo projects.

Test on Your Code

AI tools perform differently on different codebases. Test on your actual projects, not the vendor's examples.

Measure Impact

Track how much time you actually save. Are you writing code faster? Spending less time debugging? Or are you spending more time reviewing AI suggestions?

Consider Team Fit

Some tools work better for experienced developers who can spot bad suggestions. Others are better for beginners who need more guidance.

The Practical Implementation Strategy

Week 1: Pick One Tool

Choose one tool that solves your biggest daily annoyance. Don't try to optimize everything at once.

Week 2: Use It Consciously

Pay attention to when it helps and when it doesn't. Note what types of tasks it handles well.

Week 3: Measure Results

Are you actually more productive? Are you learning new patterns? Or are you just typing less?

Week 4: Expand or Replace

If it's working, consider adding complementary tools. If it's not, try something else.

The Things That Matter More Than Tool Choice

Understanding What You're Building

AI tools can't help you if you don't understand the requirements. Spend time understanding the problem before reaching for tools.

Reading the Code AI Generates

Never accept AI suggestions blindly. Always understand what the code does and why it works.

Staying Current

AI tools change rapidly. What works today might not work tomorrow. Stay flexible and keep evaluating.

FAQ: Choosing AI Development Tools

Q: Which AI tool should I start with?

A: Start with GitHub Copilot if you want code completion, or ChatGPT if you want help with debugging and explanations. They're both widely used and relatively reliable.

Q: Are AI tools worth the cost?

A: If they save you more than 2-3 hours per month, they're usually worth it. But measure actual time saved, not just typing reduction.

Q: Should I trust AI-generated code?

A: Never trust it blindly. Always review, test, and understand what the code does. AI tools are assistants, not replacements for thinking.

Q: How do I know if an AI tool is making me a worse developer?

A: If you're accepting code you don't understand, or if you can't solve problems without the tool, it might be hurting your growth. Use AI tools to be more productive, not to avoid learning.