AI for Software Development: 8 Essential FAQs Every Developer Needs
Everyone's asking whether AI will replace developers. That's the wrong question.
The right questions are smaller, more practical, and way more interesting: What can AI actually do today? What breaks? Where does it fail spectacularly? And most importantly—why does the same AI tool work brilliantly for your teammate but produce garbage for you?
After watching hundreds of developers integrate AI into their workflows, I've noticed the same questions coming up. Not "will AI take my job" nonsense, but real technical questions about where these tools actually help and where they fall flat.
Let's go through them.
1. Why Does AI Work Great in Tutorials But Fails in My Actual Codebase?
This is the number one complaint. You watch someone use Cursor or Copilot to refactor a component in 30 seconds. Magic. Then you try it in your codebase and get suggestions that don't compile, imports that don't exist, and API calls to methods you deprecated six months ago.
Tutorial codebases are small, self-contained, and recently written. Your production codebase has 400,000 lines of code, three different authentication patterns (because of that acquisition), and a utils folder that's basically a war crime.
AI models have a context window—think of it as working memory. GPT-4 can handle about 128k tokens, which sounds like a lot until you realize that's roughly 100,000 words of English text. But code is denser. With imports, comments, and structure, you're looking at maybe 30-40 files max before you hit the limit.
Your codebase has thousands of files.
The AI doesn't know about your custom error handling middleware. It hasn't seen your internal API client wrapper. It's making suggestions based on generic patterns from its training data, not the actual conventions your team uses.
This is why tools like glue.tools exist—to index your entire codebase and surface the relevant context when you need it. Instead of hoping the AI happens to see the right files, you give it the actual architecture, the patterns you actually use, and the dependencies that actually matter.
2. Should I Use AI for Code Review?
Use it, but don't trust it.
AI is excellent at catching shallow issues: unused variables, potential null pointer exceptions, obvious security patterns like SQL injection vulnerabilities. GitHub Copilot's code review suggestions will find the stuff that linters miss but that any decent senior engineer would catch.
Where AI fails is understanding business logic and system architecture. It can't tell you that your new caching layer will cause race conditions with the background job processor. It won't notice that you're about to introduce a circular dependency that'll make testing hell.
The best use case: Run AI review first to catch the obvious stuff, then have humans focus on the architectural and business logic concerns. Don't waste your team's time on "you forgot to handle the error case" when AI can catch that.
But never merge based on AI approval alone. I've seen too many subtle bugs introduced because an AI reviewer gave a thumbs up to code that technically worked but violated critical system assumptions.
3. Can AI Generate Documentation That Doesn't Suck?
Yes, but only if you set it up right.
Most AI-generated documentation sucks because it describes what the code does, not why it exists or how it fits into the larger system. You get comments like "This function adds two numbers" when what you actually need is "This calculates the weighted average for risk scoring in the loan approval pipeline."
The solution: Give the AI more context about your system architecture and business domain. This is where code intelligence platforms shine. When glue.tools generates documentation, it's not just looking at individual functions—it's mapping how features flow through your system, understanding which services depend on what, and documenting based on actual usage patterns.
For example, instead of documenting a REST endpoint in isolation, you want to know: Which frontend components call this? What database tables does it touch? What other services does it integrate with? That's documentation developers actually use.
The other trick: Use AI to generate first drafts, then have domain experts add the "why." AI can structure and format documentation consistently. Humans add the tribal knowledge about why we made certain decisions and what footguns to watch out for.
4. How Do I Know If My Team Is Actually Using AI Tools Effectively?
This one's sneaky. Everyone says they're using AI, but often they're just letting autocomplete run and accepting suggestions blindly.
Effective AI usage looks different:
You're using it to explore unfamiliar APIs or libraries faster
You're having it generate test cases for edge conditions you might miss
You're using it to refactor legacy code with confidence
You're asking it to explain complex codebases instead of spending hours reading docs
Ineffective usage:
Accepting every suggestion without reading it
Using it to write code you don't understand
Treating it like a senior developer instead of a very fast junior
Never verifying the patterns it suggests are actually correct
The biggest indicator of effective usage: Your team's code reviews start focusing more on architecture and less on syntax. When AI handles the boilerplate and obvious patterns, humans can focus on the hard problems.
Track what I call "AI-assisted complexity"—are developers taking on more ambitious refactors because they have AI backing them up? Are they exploring more solutions before committing to one? That's effective usage.
5. What About Code Quality—Does AI Make It Better or Worse?
Both, depending on how you use it.
AI tends to generate very average code. Not terrible, not excellent. It follows common patterns, which is great when common patterns are what you want. But it also means you get generic solutions to specific problems.
The quality problem shows up in three ways:
Consistency: AI will happily generate five different error-handling patterns in the same file. It doesn't maintain a coherent style unless you explicitly enforce it.
Complexity: AI loves to over-engineer. Ask it to add a feature and you'll get an abstract factory pattern with dependency injection when you just needed a simple function. This is because its training data includes lots of enterprise codebases that love unnecessary abstraction.
Technical Debt: AI doesn't understand that some code is deliberately simple because the team decided against more complexity. It'll suggest "improvements" that add dependencies or patterns you intentionally avoided.
The fix: Use code intelligence tools that understand your team's actual patterns and complexity metrics. If your codebase values simplicity, you want AI suggestions filtered through that lens. When glue.tools maps code health with churn and complexity metrics, you can catch when AI suggestions would increase complexity in already-fragile areas.
6. How Do I Get AI to Understand Our Internal Libraries and Patterns?
This is the context problem again, but more specific.
Most teams have internal libraries that form the backbone of their architecture. A custom ORM, a standardized logging format, internal API clients, shared UI components. AI trained on public GitHub repos has never seen your internal patterns.
There are a few approaches:
Fine-tuning: Expensive and overkill for most teams. You're training a new model on your codebase. Only worth it if you're a massive company with very unique patterns.
RAG (Retrieval Augmented Generation): This is the practical solution. You index your codebase and relevant documentation, then inject that context into the AI's prompts. When a developer asks about authentication, the AI sees your actual auth implementation, not generic OAuth examples.
MCP (Model Context Protocol): The newer approach that tools like Cursor and Claude are adopting. Instead of copying your entire codebase into every prompt, MCP lets AI tools query your codebase dynamically. They ask for what they need when they need it.
This is where platforms like glue.tools become critical infrastructure. You're not just indexing code—you're building a queryable representation of your architecture that AI can actually use. The AI can ask "show me all authentication middleware" and get accurate results from your actual codebase.
7. Should Junior Developers Use AI Differently Than Seniors?
Absolutely, and this is controversial.
Junior developers learn by making mistakes and understanding why something doesn't work. If AI autocompletes everything, they never build that intuition. I've seen juniors who can ship features quickly with AI but can't debug when things break because they don't understand the underlying patterns.
The recommendation for juniors: Use AI as a rubber duck and teacher, not a code generator. Ask it to explain concepts. Have it generate examples you then rewrite yourself. Use it to explore different approaches, then implement the one you understand best.
For seniors, AI is a force multiplier. You know what good looks like, so you can spot when AI suggestions are wrong. You can use it to prototype faster, handle boilerplate, and explore APIs you're unfamiliar with without losing velocity.
The worst case: Juniors using AI to write code they don't understand, then seniors spending more time in code review explaining why it's wrong. That's slower than just writing it correctly the first time.
8. What's the Actual ROI of AI Coding Tools?
This is where people get weirdly vague. "30% productivity increase!" based on measuring lines of code, which is a terrible metric.
Real ROI comes from specific use cases:
Documentation: Massive win. AI can generate and maintain documentation at a scale humans never could. Not perfect, but 80% of the way there is infinitely better than outdated or missing docs.
Code exploration: Huge time saver for understanding unfamiliar codebases. Instead of grepping through files for hours, you ask questions and get relevant code with context.
Boilerplate reduction: Writing CRUD endpoints, test setup, configuration files—AI handles this faster than you can type. Pure time savings.
Refactoring confidence: This is underrated. When you have AI-powered tools that understand your entire codebase, you can refactor more aggressively because you can see all the implications.
The hidden cost: Context switching. If using AI tools requires jumping between multiple applications or waiting for slow responses, you lose the productivity gains. This is why integrated solutions matter—whether that's in your IDE or through platforms that connect everything together.
At glue.tools, we see the ROI most clearly in reduced onboarding time and faster feature discovery. New developers can navigate a codebase in days instead of weeks. Teams can find all implementations of a feature scattered across services without manual searching.
The Real Question No One's Asking
All these questions point to a deeper issue: We're treating AI as a tool when it's actually infrastructure.
You wouldn't ask "Should I use version control?" or "Should I have a CI/CD pipeline?" Those are infrastructure decisions that enable everything else. AI for software development is the same.
The question isn't whether to use AI. It's how to build the context layer that makes AI actually useful for your specific codebase, your specific patterns, your specific team.
That means investing in code intelligence—understanding your architecture, mapping dependencies, tracking complexity, maintaining documentation. AI is only as good as the context you give it.