AI for Developers: Code Review, Debugging, Documentation, and Integrated Workflows

Writing code is only one part of software development. Most of the time goes into reviewing changes, tracking down bugs, maintaining documentation, and cleaning up technical debt. These are also the areas that slow teams down the most under delivery pressure.

AI is useful here not because it replaces engineering judgment, but because it removes repetitive work around it. The value is not in what AI can do in isolation, but how it fits into existing development workflows.

This guide focuses on practical ways to integrate AI into four core activities: code review, debugging, refactoring, and documentation.

How to Use AI Across Your Development Workflow?

Most developers reach for AI when they are stuck. The bigger opportunity is using it before you get stuck: before the pull request goes out, before the bug makes it to production, before the documentation falls three versions behind.

Each workflow below covers a specific part of the development process, with a clear process to follow and the tools that are actually worth using for that task.

AI-Assisted Code Review

Most developers run AI on their code and get back vague feedback that does not tell them anything they did not already know. The difference is in how you structure the review before it starts. Treating AI as a second pass before the human reviewer sees the code is where it actually changes the outcome.

Before opening the pull request, paste the diff or the relevant functions into your AI tool and give it a specific brief. Without context, the output is generic. With context, it is actionable.

A prompt that works:

"Review this [language] function for security vulnerabilities and edge cases not covered by the existing tests. It handles [what it does] and runs on every [trigger]."

Work through the output yourself, then open the pull request. The human reviewer now starts from code that has already been through one pass, and their attention goes toward what AI cannot assess: architectural decisions, business logic, and codebase-specific context.

Run a failure-mode pass as a separate step. After the general review, ask AI specifically to identify inputs that are not handled, edge cases that would cause unexpected behaviour, and conditions under which the function would fail silently. This catches a category of bugs that both the author and the reviewer miss because both are focused on the intended path, not the failure paths.

Where AI is consistently reliable:

• SQL injection risks, unsafe input handling, and hardcoded credentials
• Inefficient queries, unnecessary re-renders, blocking async operations
• Missing or incomplete error handling
• Naming and style inconsistencies
• Functions that need tests but do not have them

Once a gap in test coverage is flagged, ask AI to write the missing tests in the same session.

Tools

In modern AI-assisted development workflows, different tools excel at different layers of code understanding. Some focus on real-time, line-level feedback, while others are stronger at broader, cross-file reasoning.

• Cursor is the standard for in-editor review. It catches issues as you write rather than after, with inline suggestions that are directly tied to the exact line of code you’re working on.
• Claude Code is better for large-scale code understanding. For changes spanning multiple files or requiring insight into call chains across a codebase, it provides stronger context handling than other tools currently available.
• For quick reviews and summaries without leaving a single interface, Chatly's AI Coder is a fast option that combines review, generation, and multi-file analysis in one place.

AI-Assisted Debugging

The hardest part of debugging is not applying the fix. It is figuring out what is actually causing the error after you first see it. AI can significantly reduce that delay, but only when you provide the right context from the beginning.

Give AI everything at once: the error message, the relevant code, and a clear description of what you expected versus what happened.

"Here is the error: [error message]. Here is the relevant code: [code]. I expected [behaviour]. Instead I am getting [behaviour]. What is causing this?"

Ask for the cause before the fix. This is the single most important habit to build. If you ask for a fix directly, you get a fix. If you ask for the cause first, you can verify whether it makes sense for your specific context before applying anything. A fix based on a wrong diagnosis often introduces new problems that are harder to trace than the original one.

If the first diagnosis does not fit, do not re-ask the same question. Add more context: the framework version, what the function is actually being called with, what changed recently, and what you have already tried. More context produces a materially different response.

For unexpected behaviour with no clear error message, ask AI to walk through what the function does step by step before asking what is wrong. This surfaces where the logic diverges from the expected path, which is harder to spot when you just paste the function and ask for a diagnosis.

Tools

Cursor is the go-to for in-editor debugging with inline explanations tied to specific lines. Claude Code handles cases where the root cause spans multiple files or requires understanding how something is called across the codebase.

For errors that might be known library bugs or documented issues with a specific version, Chatly's AI search retrieves answers grounded in current documentation and community discussions, which matters for issues that postdate the model's training data.

AI for Refactoring and Technical Debt

Technical debt is the extra work created when code is written quickly to meet deadlines instead of being fully cleaned up or optimized. It builds up over time and has to be fixed later.

Technical debt does not build up because developers do not care. It builds up because refactoring has no deadline, while shipping features always does. AI makes it fast enough to handle in small pieces during a normal sprint instead of needing a separate project that rarely gets prioritized.

Work one function or module at a time. Trying to tackle the whole codebase at once is what turns refactoring into a project. Keeping it scoped to a single function means it fits inside a normal sprint without disrupting delivery.

For each function:

1. Paste the function into AI with a description of the specific problem: too many responsibilities, hard to read, slow query, or inconsistent with the rest of the codebase
2. Ask for a cleaner version and an explanation of what changed and why
3. Read the suggestion before applying it. AI-generated refactoring can improve readability while introducing a subtle behavioural change, or optimize for the wrong constraint, given how the function is actually used

A prompt that works consistently:

"This function handles [what it does]. The specific problem is [too many responsibilities / hard to read / slow query / inconsistent with the rest of the codebase]. Suggest a cleaner version and explain what you changed and why."

The explanation matters as much as the suggestion. Understanding what changed and why is what lets you catch cases where the refactoring is technically cleaner but wrong for your specific context.

Tools

For targeted improvement suggestions on a specific function, Chatly AI Coder helps refine and optimize code without giving a broad, general overview. For quick answers to technical questions that come up mid-refactor, such as unfamiliar patterns, API conventions, or framework behaviour, it provides focused responses without breaking your workflow.

For larger refactoring tasks that require understanding how a function is used across the entire codebase before suggesting changes, Cursor and Claude Code handle that scope better than single-file tools.

AI for Code Documentation

Documentation falls behind because writing it from scratch is slow, and there is always something with a higher priority. AI changes the economics of this by generating a structured first draft from the code itself, from engineering notes, or from a brief description of what something does.

The draft still needs an accuracy review, but the time to get from nothing to something usable is a fraction of writing from scratch.

Give AI real source material rather than asking it to generate documentation from scratch. The code, a description of what it does and who uses it, and the format you want are enough to produce a first draft that is accurate enough to review rather than rewrite.

This works well for:

• API reference documentation
• README files
• Function-level inline documentation
• Onboarding guides for new team members
• Release notes built from commit messages or engineering notes

For procedural documentation, where a wrong step causes real problems for the reader, the accuracy review is non-negotiable. For reference documentation, the draft is usually close enough that the review pass is quick.

For a complete walkthrough of turning source material into documentation your team will actually use, see our guide on how to use AI to write technical documentation your team will actually read.

Tools

For keeping documentation in sync with a codebase that changes frequently, Chatly AI Document Generator uses repository-level context to generate documentation that reflects how functions are actually used rather than just what they contain.

Conclusion

Good development practice is not hard to follow. It is just hard to follow consistently under delivery pressure. AI removes that excuse. Start with whichever part of your workflow costs you the most time right now, and build from there.