AI Coding Workflow Optimization FAQ: Expert Answers Guide
Everyone's using AI coding assistants now. Most people are using them wrong.
I've spent the last year watching teams adopt Copilot, Cursor, and Claude. The pattern is always the same: initial excitement, then frustration, then either abandonment or a slow grind toward something that actually works.
The problem isn't the AI. It's that nobody prepared their codebase for AI. Nobody optimized their workflow. Nobody asked the right questions until it was too late.
Here are the questions you should be asking, with answers that actually help.
Why does my AI assistant keep generating code that doesn't match our patterns?
LLMs are trained on GitHub's greatest hits. Your codebase is not on that list. When you ask Claude to "add authentication," it's going to generate something generic based on what it saw in training. Maybe Express middleware. Maybe a decorator pattern. Probably not the exact flavor of JWT validation with custom claims that you've been using for three years.
The fix isn't better prompts. It's better context.
You need to feed your AI assistant examples of what good looks like in your codebase. Reference implementations. The actual auth module you use. The way your team handles errors. The specific logging format that makes it through your observability stack.
This is where most teams hit the context assembly problem. You can't manually copy-paste ten files into every prompt. You'll spend more time finding examples than writing code.
Glue solves this by indexing your entire codebase and letting you quickly pull relevant context. Instead of hunting through repos, you ask "show me how we do authentication" and get actual examples from your code. Then you feed that to your AI assistant.
But even without specialized tools, you can start building a context library. Keep a docs folder with canonical examples. Update it when patterns change. Make it searchable.
How do I prevent AI-generated code from increasing technical debt?
You don't prevent it. You detect it early and fix it fast.
AI-generated code creates a specific flavor of technical debt: it works, but it doesn't fit. The logic is fine. The patterns are off. Six months later, you have three different ways to do the same thing because three different developers asked their AI assistant for help.
The solution is continuous code review focused on consistency, not correctness. Your AI assistant probably generated working code. The question is whether it's your team's working code.
Set up linting rules that enforce your patterns. Write custom ESLint plugins if you need to. Use architecture decision records (ADRs) and reference them in code reviews.
But here's the thing: you need to know what your patterns are first. Most teams can't articulate their own conventions until they see them violated.
Map your codebase. Identify your most stable, well-maintained modules. Those are your patterns. Make them visible. When someone (or their AI assistant) deviates, you'll know immediately.
Glue's code health mapping shows you which modules are stable and which are churning. High churn + low test coverage + multiple owners = pattern chaos. That's where AI-generated code will cause the most damage. Protect those areas first.
Should I use AI for legacy code refactoring?
Yes, but not the way you think.
Don't ask AI to refactor your legacy code directly. It will hallucinate. It will miss edge cases. It will confidently remove the one weird hack that keeps production running.
Use AI to understand legacy code, then guide your own refactoring.
Feed a gnarly module into Claude. Ask it to explain what the code does, line by line. Ask about the edge cases. Ask why certain checks exist. You'll spot the load-bearing hacks. You'll understand the implicit contracts.
Then use AI to generate tests. Give it the module and ask for comprehensive test coverage. Review those tests carefully. They're probably wrong in interesting ways, but they'll reveal assumptions and edge cases you missed.
Only after you have tests should you start AI-assisted refactoring. And do it incrementally. One function at a time. With human review between every change.
The best workflow I've seen: AI generates explanation → human writes tests → AI suggests refactor → human reviews and adjusts → run tests → repeat.
This takes longer than "AI, refactor this file." But it works. The fast way doesn't work.
How do I get my team to adopt AI tools without chaos?
Start with documentation, not code generation.
The highest-value, lowest-risk AI workflow is using it to explain existing code. Everyone on your team can benefit from that immediately. No merge conflicts. No broken builds. No arguments about whether AI-generated code is "real" engineering.
Pick your most complex, least-documented modules. Have team members use AI to generate explanations. Review those explanations together. Update them into actual docs.
This builds AI literacy without risk. Your team learns how to prompt effectively. They learn what AI is good at (explaining patterns) and what it's bad at (understanding business context).
Once everyone's comfortable with AI as a comprehension tool, introduce code generation gradually. Start with boilerplate. Then tests. Then feature code, with mandatory human review.
The teams that succeed with AI tools are the ones that treat adoption like a technical rollout, not a productivity hack. You need guidelines. You need training. You need a way to measure what's working.
What metrics should I track for AI-assisted development?
Forget velocity. Track quality and consistency.
Most teams measure AI adoption by tracking how much code was generated by AI. This is useless. Bad code ships fast too.
Instead, track:
Churn rate on AI-generated code. If files created with AI assistance get modified 3x more often than human-written files, your AI workflow is generating work, not eliminating it.
Pattern compliance. Are AI-generated pull requests consistent with your codebase patterns? Set up automated checks and track the failure rate.
Review cycles. Do AI-assisted PRs need more review rounds than regular PRs? If yes, you're not saving time.
Bug density. Are bugs more common in AI-generated code? Track this per module, not globally. Some areas are safer for AI assistance than others.
The goal isn't to maximize AI usage. It's to identify where AI helps and where it hurts.
Glue's team insights can show you which modules have high churn and complexity—exactly where AI-generated changes are most likely to cause problems. You can adjust your workflow accordingly.
How do I maintain context across multiple AI conversations?
You probably can't. Work with it, not against it.
LLMs don't remember previous conversations in any meaningful way. Each session is fresh. You can dump previous context into the window, but you're burning tokens and degrading quality.
Better approach: maintain context externally.
Keep a running doc of decisions made during AI-assisted development. When you start a new session, you're not asking the AI to remember. You're giving it a fresh brief: "Here's what we decided last time. Here's what we're doing now."
For complex features spanning multiple sessions, create a feature brief. Update it as you go. Use it to bootstrap new AI conversations.
Some teams are experimenting with MCP (Model Context Protocol) to let AI assistants query codebases directly. This is promising. Instead of cramming context into prompts, the AI can pull what it needs when it needs it.
Glue supports MCP integration, letting AI assistants query your codebase structure, documentation, and code health metrics on demand. This turns your AI assistant into something more like a colleague who can look things up, rather than a consultant who needs to be briefed from scratch every time.
But even without that, external context management helps. Keep docs. Update them. Reference them.
Is AI coding just autocomplete on steroids?
No. It's autocomplete plus code search plus pair programming plus documentation.
The developers who get the most value from AI tools aren't using them to go faster. They're using them to work differently.
Example: you're adding a feature that touches an unfamiliar part of the codebase. Old workflow: grep for relevant files, read code, ask teammates, make changes, hope you didn't break anything.
New workflow: ask AI to explain the module structure, identify the key files, summarize the business logic, suggest where your feature fits, generate a test that validates your understanding, then make changes.
You're not moving faster. You're moving with more confidence. You're learning the codebase as you work.
The best AI workflows reduce cognitive load. They let you focus on the hard problems—architecture, business logic, edge cases—while the AI handles the mechanical stuff.
But this only works if your AI assistant has good information about your codebase. Generic LLM knowledge isn't enough. You need codebase-specific context.
What's the biggest mistake teams make with AI coding tools?
Treating them like magic instead of tools.
AI won't fix your messy codebase. It won't compensate for poor documentation. It won't eliminate the need for code review or testing.
It will amplify whatever you already have. Good patterns? AI will replicate them. Bad patterns? AI will replicate those too.
The teams winning with AI tools are the ones who did the work first: documented their patterns, cleaned up their architecture, established clear conventions. AI made them faster. It didn't make them better.
The teams struggling with AI tools are the ones hoping AI will solve problems they haven't solved themselves. It won't.
Start with visibility. Understand your codebase. Map your patterns. Identify your gaps. Then bring in AI to accelerate the work, not replace the thinking.
That's the real optimization. Not better prompts. Not faster models. Understanding what you're building and why, then using AI to build it better.