The Best AI for Coding Right Now (And Where It Still Falls Short)

We can’t agree more on how AI coding tools are reshaping how developers are creating code and how senior developers are managing teams and implementing coding practices. These tools have become a part of the daily workflow for many devs.

Being a Technical Lead engineer myself, I have integrated some of the best tools in the market—tools often recommended by developers on forums like Reddit or Medium. Most of them are great at assistance; however, there are some scenarios where I feel the tool could have been better.

But that’s not the end. I have found and loved Qodo—it's the closest to the expectations I have for a tool to become the best AI for coding right now. I won’t say that Qodo is smarter than the rest, but what I really liked is how it’s built for large repos, supports team-level best practices, and helps with real code review workflows.

In this blog, I’ve decoded the best of Qodo and where it can still feel like you need to do some manual work. So let’s get started.

What AI Tools Are Good At Right Now

Let’s understand what AI tools are currently good at for engineering teams:

Writing Predictable Code Patterns

If I’m creating a new FastAPI route or writing a pytest fixture, tools like Copilot or Claude Code handle that well. They’re fast and autocomplete things like:

• Route decorators (@app.get("/items"))
• Function signatures (def get_items():)
• Boilerplate response models
• Pytest fixtures and setup functions

However, the challenge comes when changes span multiple layers—say, updating models, serializers, and controller files all at once. In those cases, autocomplete alone isn’t enough.

That’s where Qodo shines: it understands broader context and generates consistent code across files.

Where Qodo Makes a Difference

Qodo uses Retrieval-Augmented Generation (RAG). Instead of just predicting based on local context, it:

1. Indexes your entire repo
2. Retrieves relevant files, logic, and best practices
3. Generates aligned suggestions

Real-World Example: Django Billing Service

I had a codebase for a Django-based billing service. I needed to implement a new PaymentWebhookView to handle webhook events from a third-party payment provider.

Tasks included:

• Parsing payloads
• Validating event types
• Updating payment status
• Logging outcomes

I used [Qodo Gen](https://www.qodo.ai/products/qodo-gen/) and ran the /implement command with a brief description.

Result:

Qodo generated code using our preferred patterns:

• Used PaymentWebhookSchema for validation
• Called existing payment.services.process_webhook()
• Handled edge cases and returned proper error responses

After reviewing the code, I realized one edge case was untested. I used /add_tests, and Qodo:

• Generated two parameterized tests
• Followed our naming conventions
• Fit directly into our existing test module

All of this happened inside the IDE—no copy-pasting between tools.

Custom Rules That Fit Your Codebase

Qodo stands out because it’s review-first and team-configurable. You define what "good code" means.

Our Team's Custom Best Practices

• Every FastAPI route must return a typed ResponseModel, never raw dicts
• Database writes must go through a repository layer, not inline ORM calls
• New endpoints must include tests for invalid payloads and 500 errors

We codified these into Qodo’s best practices. When someone creates a PR, Qodo:

• Validates it against team rules
• Suggests fixes
• Provides context in reviews
• No need to wait for a human reviewer

Repo-Wide Context

Most AI tools only look at the current file. Qodo indexes the entire repo, so it understands cross-file dependencies.

Example

A teammate refactored a function in billing/payments/utils.py.

Qodo flagged a broken call in core/transactions/views.py—even though that file wasn’t touched in the PR.

That level of awareness hasn’t shown up in Copilot or Claude yet.

Where You’d Still Need Help

Qodo isn’t built for everything. Here’s what it’s not optimized for:

Open-Ended Code Generation

Qodo works best within your repo and team workflows—not for one-off ideas or abstract prompts.

If I’m trying out a new library or writing quick prototypes, I’ll use lighter tools or just code directly.

Outside the Review Loop

Most tools suggest code in isolation. Qodo is different—it’s built for how real teams ship software, especially where:

• Code standards matter
• Architecture must stay consistent
• Test coverage is a requirement

Why I Still Prefer Qodo for Enterprises

Unlike tools that rely on a single prompt-response cycle, Qodo Gen uses a multi-agent system for complex tasks.

Features That Stand Out

• Breaks tasks into substeps
• Fetches relevant code context via RAG
• Coordinates changes across files
• Integrates team-defined best practices
• Adapts to custom patterns (e.g., error handling, testing structures)

And most importantly, it uses your repo context in real time.

Works Great for

• Monorepos
• Legacy codebases with outdated dependencies
• Codebases with multiple interdependent modules

Final Thoughts

AI coding tools have come a long way—but they’re not here to replace engineering judgment.

The real value lies in:

• Handling repetitive tasks
• Reducing review friction
• Keeping teams aligned as codebases scale

That’s why I keep coming back to Qodo.

It focuses on what slows teams down—boilerplate code, review policies, test enforcement—and helps you move faster without sacrificing quality.

In a team setting, that balance really matters.