GitHub Copilot best practices: 10 tips & tricks that actually help

Copilot has quickly become a staple in the modern developer’s toolkit. Powered by OpenAI’s models, it offers AI-driven code suggestions based on what you’re writing — right in your editor. Used well, it can significantly boost productivity. Microsoft’s data suggests it may help developers code up to 55% faster.

But here’s the catch: Copilot isn’t a magic wand. Left on autopilot, it can feel more like an eager junior dev making confident guesses than a reliable coding partner. The difference between a helpful Copilot and a frustrating one often comes down to how you use it — and whether you’ve built a workflow that plays to its strengths.

In this article, we’ll walk through how Copilot fits into the broader AI dev tool stack and share practical GitHub Copilot tips and tricks for using it more effectively. These strategies are drawn from both our own experience and the thousands of developers using CodeRabbit’s AI code review platform. With the right approach, Copilot can go from a neat autocomplete toy to a genuinely valuable part of your daily development routine.

GitHub Copilot tip 1: Play to Copilot’s strengths

Not every coding task is equal in Copilot’s eyes. One of the most important GitHub Copilot best practices is to use it where it shines, not force it to create code where it doesn’t. Copilot excels at specific categories of tasks that can save you significant time.

Copilot is especially good at…

• Writing repetitive code

• Generating unit tests

• Debugging syntax issues

• Explaining code

• Generating regex patterns

These are areas where it has seen lots of examples and can confidently suggest solutions.

For example, if you have a function and need to write several tedious unit tests for it, Copilot can draft them in seconds. Consider this simple function and tests:

def multiply(a, b):     """Return the product of a and b."""     return a * b

Copilot can help create unit tests for the above function quickly:

In a scenario like the above, Copilot generated the TestMultiply class almost entirely from a comment or prompt. It’s excellent for boilerplate code, repetitive patterns, and well-defined algorithms.

On the flip side, Copilot is not a silver bullet for everything.It’s not designed to handle tasks unrelated to coding (don’t expect it to plan your database schema or design UI work) and won’t replace your problem-solving skills.

Think of Copilot as a junior developer at your side. It’s fast and often right about everyday tasks, but you (the senior developer) are still in charge of decision-making and critical thinking. Use Copilot for the “heavy lifting” on mundane code and let it suggest solutions for routine problems, but always apply your judgment on whether to use those suggestions. That way, you’ll save time and reduce drudgery while keeping yourself focused on the challenging problems and design decisions.

GitHub Copilot tip 2: Provide ample context (open files, set imports, etc.)

A hot tip for GitHub Copilot is to open all the relevant files in your project when you’re coding a particular feature. That’s because Copilot works by looking at the context in your editor to predict what you might want next. The more relevant context you give it, the better the suggestions.

For instance, if you’re implementing a function in utils.py that interacts with models.py, have both files open.

Copilot will process all open tabs (often called “neighboring tabs”) to inform its suggestions. This broader view helps it understand your project structure and produce more accurate code. In fact, simply opening related files in VS Code or your IDE can significantly enhance Copilot’s completions by providing extra context for definitions and usages across your project.

Similarly, explicitly set up your imports, includes, and dependencies before expecting the best suggestions. You know what libraries or frameworks you intend to use – tell Copilot by importing them at the top of your file. This gives Copilot a heads-up on what tools it should use.

It’s often best to manually add the modules or packages (with specific versions, if needed) before asking Copilot to generate code using them. By doing so, you avoid Copilot defaulting to an outdated library or missing an import.

For example, if you plan to use pandas in your code, write import pandas as pd yourself; then when you ask Copilot to manipulate a DataFrame, it will already know to use pandas and won’t attempt a pure-Python solution or an incorrect import.

Also, be mindful of irrelevant context. Copilot’s window of attention is limited. If you have a lot of unrelated files open or leftover code in your editor, close or remove them when you switch tasks. Keeping only the pertinent files and context visible ensures Copilot isn’t “distracted” by code that doesn’t matter to your current goal.

GitHub Copilot tip 3: Write descriptive comments and docstrings as prompts

You understand prompt engineering when you’re directly calling an LLM. But did you know that there are some sneaky ways to prompt engineer in Copilot? One of the most effective GitHub Copilot tips is to guide the AI with natural language comments.

Think of writing comments as a form of prompt engineering. Before you write the code, describe in plain English (or your preferred language) what you intend the code to do.

For example, we want a function to sort a list of names case-insensitively. We might start with:

The moment you write that comment and pause, Copilot will likely suggest the rest of the function (e.g., using sorted(names, key=str.lower)). A top-level comment at the start of a file, or a docstring/comment above a function, helps Copilot understand the overarching objective before diving into implementation details.

This process is similar to giving a human colleague a quick overview of the task at hand, it sets the stage so the following code makes sense in context.

When writing these comments, be clear and specific about the desired behavior. Mention any requirements or constraints. For a more complex example, suppose you need a function to format a person’s name as "LASTNAME, Firstname".

You could provide an example in the comment to clarify your intent:

By including the example of input and output, you give Copilot a crystal-clear idea of what you want.

Which is exactly the desired solution. The first comment was a prompt describing the goal and even provided a test case, and Copilot filled in the implementation.

Use this technique liberally. Add a brief docstring or comment for each function describing what it should do (and how at a high level, if you have an approach in mind). Copilot can detect the comment syntax for your language and will often even help complete the comment if it recognizes a pattern (for example, it might suggest a template for a Python docstring).

By writing specific, well-scoped comments before the code, you essentially “program” Copilot with your intent.

Remember the old saying: garbage in, garbage out. If you feed Copilot an ambiguous comment like “# do something with data”, you’ll get ambiguous code. Instead, describe the task clearly – “# Calculate the average value from a list of numbers, ignoring any nulls” and watch Copilot more reliably produce the correct logic.

GitHub Copilot tip 4: Use meaningful names for clarity

You might hate being stickler about style but variable and function names are another form of context that Copilot relies on.

A tip that might seem obvious but is often overlooked is togive your functions and variables meaningful, descriptive names. If you have a function named foo() or variable data1, Copilot has virtually no clue what you intend, beyond what it can infer from a possibly sparse usage context.

In contrast, names like calculate_invoice_total() or user_email_list immediately convey intent to humans and the AI. In fact, Copilot’s suggestions will improve dramatically when your code is self-documenting.

A function called fetchData() doesn’t mean much to Copilot (or to a coworker) compared to a function named fetch_airport_list() or get_user_profile. The latter gives far more hints of what the function should do.

For example, consider these two scenarios:

Vague naming:

# Determine if a user is eligible for promotion def check(user, data):     # ...

With a function name like check and a parameter data, Copilot might struggle. “Check” could mean anything. Could it check a password or a value in data? Its suggestions might be generic or incorrect because it’s guessing your intent.

Descriptive naming:

The function name is_user_promotable clearly signals a boolean decision, and the parameters user_profile and promotion_rules indicate the data involved. Copilot can use this information to guess that you might iterate over rules, check user attributes, etc., and its completion will align with that logic.

Adopting clear naming conventions isn’t just a general coding best practice for developers. It’s a GitHub Copilot best practice, too, because Copilot can only infer intent from what it sees. If what it sees are meaningful identifiers and not cryptic ones, it will return far more relevant code. This tip also pays dividends for code maintainability – since you’ll get better AI suggestions and cleaner code for your team.

GitHub Copilot tip 5: Pair Copilot with CodeRabbit for AI-assisted code reviews

While Copilot is fantastic during the coding phase, what about after you’ve written your code? Enter CodeRabbit, an AI-powered code review developer tool that complements Copilot in the development workflow.

CodeRabbit acts like an AI “pair reviewer,” scanning your code (either in the IDE or on your Git platform) and providing feedback and suggestions for improvement.

We’ve found that using Copilot and CodeRabbit together creates a powerful feedback loop: Copilot helps you generate code quickly and CodeRabbit helps ensure that code meets quality standards before it gets merged.

• You don’t get the developer who wrote the code to review it, so why get the same AI system to do so?

• An AI code reviewer also allows you to standardize your quality gate if your team is using multiple AI coding agents – as so many teams are these days.

• Finally, purpose-built AI coding agents like CodeRabbit do a more thorough job and have more features that means the average user is able to find 50% more bugs in half the time they’d typically spend on a code review.

CodeRabbit integrates into VS Code and pull requests on platforms like GitHub. In your IDE, you can invoke CodeRabbit to review the file or the diff you’re working on. It will directly add AI-powered inline review comments in the code, pointing out potential issues, much like a human reviewer would.

For example, CodeRabbit might flag that your function lacks error handling for a specific edge case or suggest a more appropriate HTTP status code.

On GitHub or GitLab, CodeRabbit can automatically comment on PRs with its findings, saving human reviewers time by catching obvious problems first. It also provides line-by-line code reviews, highlighting possible bugs, code smells, style issues, or even missing unit tests.

How best to use Copilot and CodeRabbit together

Think of Copilot and CodeRabbit as two halves of a complete AI-assisted development cycle.

You use Copilot while writing code to speed up implementation. Then you use CodeRabbit to review that code and catch anything Copilot (or you) might have missed.

• Copilot might generate a solution that works but isn’t optimal and CodeRabbit could point out a performance issue or a more idiomatic approach.

• Copilot might not know your project’s specific coding standards, but CodeRabbit can enforce them during review. Perhaps your team prefers format() over f-strings, etc.. CodeRabbit can comment on that.

• Copilot might help you quickly whip up a new API endpoint, CodeRabbit could then run and immediately warn, “Hey, you didn’t handle the case where this input is null,” or “This SQL query might not be parameterized.” You can address those before your human colleagues even look at the code.

Essentially, Copilot gets you to a working draft faster, and CodeRabbit gives you confidence to ship it by auditing the code. It’s like having an AI pair programmer and an AI code auditor working together.

In the context of a complete AI dev tool stack, Copilot and CodeRabbit cover a lot: Copilot for coding, CodeRabbit for review, and you might even use other AI tools for testing or security.

To get started, you can install CodeRabbit’s IDE extension or add it to your GitHub repository as a GitHub App from the marketplace. We highly recommend this for teams and there’s even a 14-day trial.

GitHub Copilot tip 6: Be specific and provide examples in prompts

When it comes to guiding an AI model, specificity is king. If you’re asking Copilot to write code to transform data, consider providing a short example of the data format in a comment or docstring. If you want a function to calculate something, state the formula or an example scenario in natural language.

Copilot’s underlying model is essentially trying to predict what a knowledgeable developer would write next. If your prompt (context + comments) is vague, the model must guess and may go wrong. But if you spell out details and even including sample inputs and outputs if possible, that helps

For instance, suppose you need to parse a log line like "2025-06-02 09:00:00 - ERROR - failed to connect". Instead of just writing # parse log line, you could write:

This specific prompt gives Copilot a clear blueprint: it knows the log format and the desired output types. With the example shown, the chances of Copilot writing a correct parse_log_entry implementation (splitting by " - ", parsing the date with Datetime, strptime, etc.) are much higher. Without the example, Copilot might misidentify the format or split incorrectly.

When prompting Copilot for non-trivial code, spell out the details. If a function has constraints (e.g. “input can be null” or “assume list is sorted”), mention them. If there’s a particular approach you want (e.g. “use binary search” or “use recursion”), hint at it in your comment. And if possible, provide a quick example. The model will take these as strong cues and align its suggestions accordingly.

GitHub Copilot tip 7: Break complex tasks into smaller steps

Copilot works best when it’s dealing with a focused, well-defined task. If you ask it to do too much at once, you might get a muddled or incomplete answer. A great strategy is to break down big problems into bite-sized pieces and tackle them one by one with Copilot’s help.

For example, imagine you need to implement a complex algorithm. Instead of prompting Copilot to write the whole thing in one go (which might result in a long, confusing blob of code), start by outlining the high-level steps as comments or pseudocode.

You might write a few lines of comments or stub functions and then let Copilot fill in each part. Generate code incrementally, rather than all at once. This approach makes Copilot’s job easier (each step has more specific context). That makes it easier for you to review and trust the code at each step.

Let’s say you’re building a small command-line program. First, prompt Copilot to parse command-line arguments, then separately prompt it to implement the business logic, then prompt it to handle output. You can check Copilot’s work at each stage by breaking the flow.

Think of Copilot as participating in a step-by-step refinement of your code. So, don’t try to have Copilot write an entire module in one shot. Instead, have it write one function at a time, or even one logical block at a time, especially if the logic is intricate.

By decomposing tasks, you also naturally create opportunities to review each piece. This incremental approach leads to better quality code and fewer surprises. It’s much easier to debug a smaller Copilot suggestion than the 50-line monolith it spit out because you asked a very broad question.

GitHub Copilot tip 8: Leverage Copilot Chat vs inline completions wisely

GitHub Copilot has two primary flavors – traditional inline code completion and the newer Copilot Chat (an interactive chat interface available in VS Code, Visual Studio, and other environments).

Knowing when to use Copilot Chat vs when to rely on inline suggestions can make a big difference in your workflow. It’s one of those subtle hot tips for GitHub Copilot that can transform how you approach a problem.

Inline code completions (the original Copilot experience) are best for:

• In-the-flow coding assistance: When you’re writing code and want Copilot to suggest the next line or block as you type. This works great for completing a small algorithm, filling in a loop, or writing boilerplate in place.

• Filling in repetitive code or simple patterns: For example, generating a quick data class, an API call, or the next cases in a series of if/elif conditions. The inline suggestions excel at continuing your current context.

• Generating code from a commented intent: as we’ve seen, if you write a comment # do X, the inline completion often does X immediately in code form.

On the other hand, Copilot Chat is more powerful when you need more interaction or have questions about your code:

• Explaining or analyzing code: You can ask Copilot Chat “What does this function do?” or “Why am I getting a KeyError here?” and get a natural language answer. The chat can act like a super-smart rubber ducky for debugging.

• Larger code generation tasks with iteration: If you want to generate a sizable chunk of code (say a whole function or class) and then refine it, Copilot Chat is ideal. You might ask it to write the code, then say, “now optimize this” or “can you refactor that part using a dictionary instead of if-else?” This back-and-forth is something inline suggestions can’t do easily.

• Using personas or specific commands: Copilot Chat has a concept of keywords and skills (and allows system-level instructions like “Act as a senior developer…”) which you can use to influence its style or thoroughness. For instance, you could instruct it to be security-conscious when writing the code.

To illustrate, if I have a piece of code and I’m not sure it’s efficient, I might use Copilot Chat: “Explain the complexity of this function. How can I improve it?” Copilot Chat might identify the bottleneck and even suggest a more efficient approach.

On the other hand, if I just need the next few lines of a loop, inline completion is faster. I can just hit Tab and keep coding.

Tip: If you have access to both, don’t forget you can use them together. Maybe start writing a test function (inline completion helps you fill out test cases), then switch to Chat to ask Copilot to generate some additional tests or explain a failing test. Each tool has its sweet spot.

GitHub Copilot tip 9: Cycle through suggestions and refine your prompts

By default, Copilot might show you one suggestion – the most likely completion – for your prompt or code context. But what if that suggestion isn’t what you want? Many users forget that Copilot usually has multiple suggestions under the hood. Don’t settle for the first thing it offers if it’s not quite right. A GitHub Copilot tip that’s helped me is to use the keyboard shortcuts (or the Copilot panel) to cycle through alternative suggestions.

There might be a gem in suggestion #2 or #3 that better fits your needs than suggestion #1.

Additionally, you can open the Copilot sidebar (or the full chat interface, if available) to explicitly ask for more options. In some IDE setups, hitting a special shortcut (like Ctrl+Enter in VS Code with Copilot Chat enabled) will even reveal multiple completions at once. Scanning through a few options can save you time you’d otherwise spend editing a less ideal suggestion. It’s like getting a second and third opinion from the AI.

If none of the suggestions look good, that’s a signal to refine your prompt or add more context. Perhaps your comment was too short or ambiguous. Try rephrasing it or adding another detail and then trigger Copilot again. The model’s output can vary greatly with slight changes in how you ask.

For instance, if the #sort list didn’t give the desired result, then #sort the list of names in alphabetical order might produce a better suggestion.

Another trick is giving feedback to Copilot. If you’re using Copilot Chat or the sidebar, you might have thumbs-up/down buttons you can press to give feedback on suggestions. While this won’t instantly change the current suggestion, it does send feedback to improve the model’s future behavior through reinforcement learning. And in chat mode, you can directly say, “No, that’s not what I meant. I actually want X,” and the AI will try again.

GitHub Copilot tip 10: Review, test, and verify Copilot’s output

Copilot can generate code that looks perfect at first glance, but remember, it’s not guaranteed to be 100% correct or optimal.

Always review and test the suggestions before integrating them into your codebase. This tip cannot be stressed enough. Copilot may introduce bugs, security issues, or logically wrong code if the prompt is misunderstood. You, the developer, are the last line of defense to ensure quality.

To review Copilot’s output, first, read the code carefully and make sure you understand it. If Copilot suggests a complex algorithm or some math you’re unsure about, ask Copilot (via Chat) or use your knowledge to break down what it’s doing.

You can ask Copilot Chat to explain the suggested code in plain language as a helpful Copilot trick. Often, I’ll paste a large suggestion into the chat and prompt: “Explain what this code does.” You can immediately catch any assumptions or errors if the explanation reveals any assumptions or errors.

Next, consider edge cases and correctness.

• Does the code handle empty inputs?

• What about error conditions?

• If something looks fishy (like a potential off-by-one error or an unbounded recursion), address it or prompt Copilot to fix it.

Security and style are also important. If your prompt didn't specify otherwise, Copilot might use a deprecated function or an insecure approach. Always double-check things like SQL queries (are they parameterized to prevent injection?), file operations (are files closed properly?), and any cryptography or authentication code it writes (does it follow best practices?).

Linting and static analysis tools are your friends here. Run your linters or code formatters on Copilot’s code to catch style issues, and use any security scanners (like Snyk or CodeQL) if applicable to flag vulnerabilities.

Finally, remember that Copilot might occasionally produce code that is oddly similar to public examples (especially for very common algorithms). It’s rare, but if you’re working on a closed-source project and have strict license requirements, be mindful of this. You can configure Copilot to avoid suggestions that match public code, if needed.

Now, it’s time to use these hot tips for GitHub Copilot!

GitHub Copilot is a game-changer for developers but like any powerful tool, it yields the best results when used skillfully. We’ve covered our top 10 hot tips for GitHub Copilot – from crafting great prompts and leveraging context to integrating Copilot with an AI reviewer like CodeRabbit. By implementing these GitHub Copilot best practices, you’ll find Copilot becomes much more helpful.

It can handle the boilerplate and suggest clever solutions, allowing you to focus on higher-level thinking and problem-solving. Also, don’t forget that Copilot and the surrounding AI ecosystem are evolving rapidly and new features (like the CLI tool, vision-based Copilot, etc.) are coming out regularly. Stay curious and keep experimenting with how you use it.

Perhaps you’ll discover new GitHub Copilot tips and tricks beyond the ten we’ve shared. Or we’ll just write another article.

Interested in using CodeRabbit with Copilot? Start your free 14-day trial.