Why Cody Step by Step Coding Still Beats ChatGPT for Real Development

If you’ve spent any time in a terminal lately, you’ve probably felt the shift. Coding isn't just about typing anymore. It’s about navigating the noise of a dozen different AI assistants shouting suggestions at you. Everyone talks about GitHub Copilot or ChatGPT, but honestly, if you're trying to build something complex, you need to look at Cody step by step to actually understand how your codebase hangs together.

Sourcegraph built Cody because they realized something most of us know deep down: an AI is only as good as what it can see. If your LLM doesn't know about that weird legacy function you wrote three years ago in a different directory, it's going to hallucinate. It's going to fail. Cody doesn't just guess; it indexes your entire repository.

What is Cody anyway?

Basically, Cody is an AI coding assistant that lives inside your editor—VS Code, JetBrains, you name it. But it’s different from the "autocomplete" bots you’re used to. It uses Sourcegraph’s code search graph to pull context from everywhere.

Think about it this way.

Most AI tools are like a library assistant who has read every book in the world but has never seen your private journal. When you ask them a question about your life, they give you a generic answer based on a self-help book. Cody is the assistant who has actually read your journal. It knows your variable naming conventions. It knows your architectural sins.

Getting Started with Cody Step by Step

You don't need a PhD to get this running. First, you just grab the extension. Head to the VS Code Marketplace. Search "Cody." Hit install. Done.

Once it’s in there, you’ll need to sign in. You can use a free tier, which is surprisingly generous, or hook it up to an enterprise account if your boss is paying the bills. The magic happens during the indexing phase. Don't skip this. Cody needs to "read" your local files to build a mental map of the project.

Setting up your workspace

Open a project. Any project. Maybe that React app you abandoned six months ago. Look for the Cody icon in the sidebar. You'll see a chat interface, but the real power is in the "Context" settings. You want to make sure it’s looking at your current folder.

I’ve seen people complain that Cody gives generic advice. Usually, it’s because they haven't allowed it to index the local repository. If it’s just pulling from the general internet knowledge of the LLM (like Claude 3.5 or GPT-4o), you're missing the point.

The Chat vs. The Commands

There are two ways to use Cody step by step in your daily flow.

Chat is for the "big picture" stuff. You ask, "How do I add a new payment gateway to this specific checkout flow?" Cody will look at your services/payment.ts, your models/transaction.py, and your frontend components. It’ll literally list the files it’s referencing. It’s transparent. No magic black box nonsense.

Then there are Commands.

Commands are for the grunt work. Highlighting a messy block of code and hitting Cmd+Shift+V (on Mac) to "Explain Code" or "Document Code." It's fast. It’s surgical. Honestly, the "Find Code Smells" command is a bit of a reality check. It’ll point out where you’ve over-engineered a simple loop or left a console log that definitely shouldn't be in production.

Why the Context Engine is the Real MVP

Let's talk about RAG. Retrieval-Augmented Generation.

It sounds like tech jargon, but it’s the secret sauce. When you use Cody step by step, the tool isn't just sending your prompt to an AI. It’s searching your code for relevant snippets first. It attaches those snippets to your prompt.

Imagine you're using a specific, obscure library for handling Bluetooth signals. ChatGPT might know the documentation from 2022. But Cody sees how you implemented it in utils/bluetooth_helper.js yesterday. It uses your own logic to suggest the next step.

This prevents the "hallucination loop." You know the one. Where the AI suggests a library that doesn't exist, and then suggests a fix for the library that doesn't exist, and suddenly you've wasted two hours on a Saturday. Cody stays grounded in your reality.

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Customizing the Experience

You aren't stuck with one "brain." One of the coolest things about the latest versions is that you can swap the underlying model.

Feeling like Claude 3.5 Sonnet is better at logic today? Switch to it.
Want the raw power of GPT-4o for a complex refactor? Toggle it in the settings.

This flexibility is huge. Most tools lock you into their proprietary model. Sourcegraph is basically saying, "We provide the context; you choose the engine."

Working with Recipes

Cody has these "Recipes" which are basically pre-baked prompts.

• Generate Unit Tests: It looks at your logic and writes the test suite. It actually imports the right testing framework you're already using (Jest, Vitest, Pytest).
• Summarize Recent Changes: Great for when you come back from a long weekend and have no idea what your past self was doing.
• Improve Code Quality: This is the "be-my-senior-developer" button. It suggests better naming and more efficient patterns.

The Limitations (Let's be real)

Look, it’s not perfect. No tool is.

If your codebase is a total disaster—meaning no consistent naming and files that are 5,000 lines long—Cody will struggle to find the right context. It’s an AI, not a miracle worker. Also, the free version has rate limits. If you’re a power user doing 500 prompts a day, you’re going to hit a wall.

Privacy is another big one. If you’re at a major bank or a healthcare company, you can’t just send your code to a random cloud. You have to use the Enterprise version that keeps everything local or within your VPC.

The Step-by-Step Workflow for a New Feature

If I’m building a new API endpoint today using Cody, here is exactly how I do it:

1. The Discovery Phase: I open the Cody chat and ask, "Where are the existing API routes defined, and what is the pattern for error handling?" Cody gives me a list of files and a summary of the middleware.
2. The Scaffolding: I create a new file. I type a comment: // Create a POST route for user profiles that validates the email. Cody's autocomplete (which is separate from chat) suggests the boilerplate code based on the patterns it just found in the other files.
3. The Refinement: I highlight the generated code and use the "Fixup" command to add specific logging.
4. The Testing: I run the "Generate Unit Test" recipe. I tweak the mock data it suggests.
5. The Documentation: I use the doc-string generator to make sure the next person who touches this code knows what's going on.

What Most People Get Wrong

People treat AI like a replacement for thinking. That's the mistake.

You should treat Cody step by step like a pair programmer who has a perfect memory but occasionally gets overconfident. You still have to drive. You still have to review every line. But instead of spending 20 minutes digging through folders to find where a variable is defined, you just ask.

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It’s about reducing the "cognitive load." Coding is exhausting because of the context switching. Switching from your editor to Google to Stack Overflow and back to your editor kills your flow. Keeping everything inside the IDE changes the game.

Moving Forward

If you’re serious about integrating this into your workflow, start small. Don't try to refactor your whole app on day one.

• Download the extension: It’s on the VS Code Marketplace or JetBrains.
• Index your main project: Give it five minutes to "learn" your code.
• Try the "Explain this" command: Take the most confusing file in your project and see if Cody can make sense of it.
• Compare models: Toggle between Claude and GPT to see which one "feels" more like your coding style.

The goal isn't to write more code. The goal is to write better code, faster, without losing your mind in the process. Cody is probably the closest we've gotten to that yet.

Actionable Next Steps:

1. Install the Cody extension in your preferred IDE.
2. Sign in and allow the local indexing to finish—this is vital for accurate suggestions.
3. Use the Cmd+Shift+V (or Ctrl+Shift+V) shortcut on a complex function to see how it interprets your specific logic.
4. Experiment with the "Custom Commands" feature to automate repetitive tasks like adding specific JSDoc headers or formatting JSON structures unique to your project.