TL;DR: AI for Real Dev Work

What actually helps: Debug weird errors, explain unfamiliar code, write boilerplate, document your mess What wastes time: "Write me a complete app," asking it to architect your system, trusting it blindly Best use: Pair programming partner who's fast but sometimes confidently wrong Reality check: Still faster to write simple code yourself. AI shines on the annoying stuff.

Most AI coding tutorials show you how to build a todo app. You don't need that.

You need help when you're stuck on a weird bug at 11pm. When you're staring at someone else's code wondering what they were thinking. When you have to write documentation and you'd rather do literally anything else.

That's what this guide covers. The actual dev work where AI saves hours.

The Biggest Mistake Devs Make

Stop asking AI to write your app from scratch.

"Build me a REST API with authentication, database, and frontend in React."

What you get: Code that looks good but breaks in ways you don't understand. Security holes you don't see. Patterns you can't maintain.

Use AI for the pieces you already understand.

You know what you're building. You just want help with the parts that are tedious or unfamiliar.

Debugging (Where AI Actually Helps)

The Stack Trace From Hell

You know this situation. Error message that tells you nothing. Stack trace 50 lines deep. Google returns shit from 2015.

I'm getting this error: [paste exact error]

Code that triggers it: [relevant code only, not your whole app]

What I tried:
- [thing that didn't work]
- [other thing that didn't work]

Environment: [language version, framework version, OS if relevant]

Why this works: Gives AI context about what you already ruled out. Focuses on understanding the problem, not getting a code dump.

The "It Works On My Machine" Bug

This code works locally but fails in production: [code]

Local: [specs]
Production: [specs]
Error in production: [error]

Time saved: 30 minutes of guessing → 2 minutes getting actual leads to investigate.

The Weird API Behavior

Using [API name]. Documentation says [what docs claim].

My code: [your implementation]

Expected: [what should happen]
Actual: [what's happening]

AI often recognizes common API quirks that aren't documented.

Understanding Code (Not Writing It)

The "What The F*ck Is This" Moment

Someone left the company. Their code remains. No comments. Variable names like tmp2 and x.

What's this code actually doing? [paste the mess]

I get that it [obvious part], but:
- Why is [weird part] necessary?
- What's [confusing variable] tracking?
- What breaks if I change [suspicious line]?

Context: This is part of [system function]. Users do [action]

Better than: Spending an hour reading every line trying to trace data flow.

The New Framework You're Learning

I'm new to [framework]. Looking at this code: [example]

I understand [parts you get], confused about:
- What's [unfamiliar pattern] doing?
- Why use [approach] instead of [what you'd normally do]?
- Is this a [framework]

Focus on learning the concepts, not just fixing the immediate thing.

Writing Boilerplate (Where AI Crushes It)

CRUD Operations

Need CRUD endpoints for [resource]. Using [framework].

Schema: [your data model]

Generate:
- Create with [validation rules]
- Read with [query filters needed]
- Update with [what fields can change]
- Delete with [cascade behavior]

Include error handling for [specific cases you care about]

This is the boring stuff AI does well. You'll still review it, but beats typing it all out.

Test Cases

Function to test: [your code]

Generate test cases covering:
- Happy path
- [Edge case 1 you thought of]
- [Edge case 2]
- [Error condition you're worried about]

Use [test framework]

Database Migrations

Current schema: [existing tables]

Need to add: [new feature's data requirements]

Generate migration that:
- Adds [new stuff]
- Preserves [existing data]
- Handles [specific edge case]

Using [database] and [migration tool]

Double-check these. AI sometimes generates migrations that work but perform terribly on large tables.

Documentation (When You Have To)

The Function You Wrote Six Months Ago

Document this function: [your code]

API Documentation

Document this endpoint: [code]

Need:
- What it does
- Request format with example
- Response format with example
- Error cases with status codes
- Rate limits or auth requirements

Format: [whatever your team uses]

The README Nobody Reads

Write README for: [project description]

Code Review (AI As Second Pair Of Eyes)

Your Own Code Before Submitting

Review this code I wrote: [code]

I'm worried about:
- [Specific concern 1]
- [Specific concern 2]

Someone Else's PR

Reviewing PR that does: [what it's supposed to do]

Code: [the changes]

Don't let AI approve/reject PRs. Use it to spot issues you might miss, then use your judgment.

SQL Queries (Because SQL Syntax Sucks)

The Join Hell

Database schema: [relevant tables and relationships]

Need query that:
- Gets [what you want]
- Filtered by [conditions]
- Joined on [relationships]
- Sorted by [order]

Using [database flavor - matters for syntax]

Performance Issues

This query is slow: [your SQL]

Table sizes: [approximate rows]
Indexes: [what exists]

Git Disasters

The Merge Conflict

Merge conflict in: [file]

My branch did: [description]
Their branch did: [description]

Conflict: [show the actual conflict]

The "Oh Shit" Commit

I committed [thing I shouldn't have].

Options I know: revert, reset, rebase
Already pushed: [yes/no]

Real Prompts That Save Time

The Library You've Never Used

Using [library] for first time. Need to [task].

Their docs say [what docs claim]

The Regex You'll Never Remember

Need regex for [what you're trying to match/validate].

Should match: [examples]
Should NOT match: [examples]

Language: [matters for syntax]

The Config File

Setting up [tool/framework]. Need config for:
- [Requirement 1]
- [Requirement 2]
- [Setting you're not sure about]

What AI Gets Wrong (A Lot)

Security: AI suggests code with SQL injection, XSS, or auth bypasses pretty regularly. Always review security-sensitive code.

Performance: AI optimizes for working code, not fast code. Nested loops, N+1 queries, loading entire tables into memory - it'll do it all.

Best practices: AI often uses outdated patterns or anti-patterns for your specific framework. Just because it works doesn't mean it's how you should do it.

Dependencies: AI recommends libraries without checking if they're maintained, have security issues, or are overkill for your needs.

Error handling: AI tends toward try-catch everything, which hides problems instead of fixing them.

The fix: Use AI to get you 70% there, you handle the remaining 30% that requires judgment.

ChatGPT vs Claude for Coding

ChatGPT:

• Better at: Code generation, syntax, common patterns

• Use for: Writing boilerplate, standard implementations

• Weaker at: Explaining reasoning behind architectural decisions

Claude:

• Better at: Explaining code logic, understanding context, architectural thinking

• Use for: Understanding unfamiliar code, reviewing approaches

• Weaker at: Speed of code generation

Reality: Most devs use ChatGPT for day-to-day, Claude when they need to think through something complex.

What Not To Ask

Don't: "Design my entire system architecture" Why: AI doesn't know your constraints, scale, team, or future needs. You'll regret following this blindly.

Don't: "Write production-ready code for [complex feature]" Why: AI doesn't know your codebase standards, error handling, or edge cases. You'll spend more time fixing it than writing it yourself.

Don't: "Review this 1000-line file" Why: AI can't maintain context that long. Break it into smaller chunks.

Don't: "Fix all the bugs" Why: Too vague. Be specific about what's wrong and what behavior you expect.

Frequently Asked Questions

Will AI replace developers?

No. It replaces some of the boring parts. You still need to know what to build, how to architect it, and why the AI's suggestion is wrong half the time.

Should I feel bad about using AI for code?

No more than using Stack Overflow. It's a tool. The skill is knowing when to use it and when not to trust it.

How much faster does AI make you?

Depends on task. Boilerplate and docs: 5x faster. Complex logic: maybe 1.2x. Debugging weird issues: sometimes 10x, sometimes useless.

Can junior devs learn with AI?

Careful. AI makes it easy to copy code you don't understand. Use it to explain concepts, not to skip learning fundamentals.

What if AI gives me bad code?

It will. Regularly. That's why you read what it generates. AI is fast and dumb. You're slower and smart.

Does AI make me a worse developer?

Only if you stop thinking and just copy-paste everything. Use it to save time on tedious stuff, not to avoid learning.

Should I tell my team I'm using AI?

Depends on team culture. Most teams don't care as long as the code works and is maintainable. Some have policies. Ask.

Can I use AI in interviews?

Usually no. If you need AI to pass a coding interview, you'll struggle in the job.

Related Reading

Tool Comparison:

• ChatGPT vs Claude vs Gemini vs Perplexity 2026: Which AI Tool Is Best for You?

Prompt Basics:

• Best AI Prompts for ChatGPT, Claude & Gemini in 2026: Templates, Examples & Scorecard

Business Use:

• AI Prompts for Business 2026: ChatGPT, Claude & Gemini ROI Guide for Teams

www.topfreeprompts.com

Access 80,000+ prompts including dev-specific templates for debugging, documentation, code review, and the annoying stuff you don't want to do yourself.