Navigating the Future with AI Copilots: A Comprehensive Guide

AI copilots are popping up everywhere, changing how we work. They do the boring stuff so we can focus on the big ideas.

For an overworked developer, they are a long-awaited miracle. Work's got too much going on. Too many emails, too much data, too little time. It's tough to keep up. You're stuck doing tasks that feel like a waste of your skills.

Having an AI copilot to handle the repetitive tasks and help make better decisions faster lets you get more done and have time for creative work. With the recent AI explosion, it is no surprise that there are a ton of options out there. So, in today’s guide, we’re going to explore a few of the best AI copilots and how to use them to their full potential.

Understanding AI Copilots

In simple terms, an AI copilot is a supportive tool that utilizes artificial intelligence to augment human capabilities. It doesn’t take away your entire workload but rather helps you focus on things besides hammering out lines of code.

The technology behind AI copilots is both fascinating and complex. At the core of most copilots is a suite of machine learning algorithms and models that enable them to process and understand large datasets. These models, particularly Large Language Models (LLMs), are trained on a wide array of data sources to recognize patterns, predict outcomes, and generate human-like responses.

The impact of AI copilots is far-reaching, with a presence in sectors such as software development, healthcare, finance, and customer service. With companies like OpenAI, Google, and Microsoft all racing to develop the best possible solutions, we can enjoy the benefits of this rapidly improving technology for a wide range of use cases.

Benefits of Using AI Copilots

The benefits of using an AI coding copilot are substantial, particularly when it comes to software development. There are a lot of areas in coding that take up valuable time. Things like typing out boilerplate, templates, and frequently used code blocks — all take away from the part of software development that requires us to use our brains. A good copilot AI tool for developers can help out in all of these areas.

Increased Developer Productivity

One of the primary benefits of an AI-powered copilot is their ability to significantly boost efficiency and productivity.

Enhanced Accuracy and Decision-making

Another key advantage is the enhancement of accuracy and decision-making. AI copilots are capable of suggesting entire blocks of code and solutions to problems that may arise, which can help reduce errors and improve the quality of the final product.

Customization and Learning from User Interactions

A modern copilot AI tool is designed to learn from user interactions and adapt over time, offering personalized support that becomes more accurate and helpful with each use. For the longest time, LLMs were only good at making semi-usable suggestions. But with the increased innovation, evidenced by recent ChatGPT claims to remember user conversations, we can offload some of our brain-power to an AI coding assistant copilot.

Reduction in Repetitive Task Load

As developers, typing the same code over and over again can take the wind out of our sails. Besides making the job boring, having to do repetitive tasks drains our energy away from more important areas like creativity and strategic thinking. Copilot AI coding allows for the reduction in this repetitive task load, and tools like Pieces can even help you store code snippets you frequently reuse so you don’t have to go back and look for that perfect snippet you researched a month ago.

AI copilots take on the burden of mundane tasks, freeing up developers to focus on more complex, creative, and strategic aspects of their projects. This shift in focus can lead to more innovative solutions and advancements in the field.

Best AI Copilots on the Market

Pieces

Pieces is one of the more powerful AI copilots out there that enhances developer productivity by offering personalized workflow assistance. It's particularly good at capturing and enriching materials, streamlining collaboration, and solving complex problems through a thorough understanding of your unique workflow.

The great thing about Pieces is that it operates at the system level, integrating your workflow across the browser, IDE and collaboration software through retrieval augmented generation, which allows it to make highly contextual suggestions based on the things you're referencing and interacting with. It's available on macOS, Windows, and even Linux.

Pieces is a huge leg up for anyone looking to increase developer productivity on their team. But besides easing developer fatigue, security is also a major advantage. Unlike strictly cloud-based solutions, data security is a top priority with Pieces. Your information stays on your machine, and all large and small language models run on-device, ensuring that your work remains private and secure. And for those times when offline AI isn't an option, cloud capabilities are available as an opt-in feature.

There are more than a handful of ways to implement Pieces. You can find an extension or plugin for everything from IDEs like VS Code and JetBrains to knowledge management apps like Obsidian. Just locate the Pieces extension or plugin you need from the website, install it, and follow any onboarding instructions provided. The process is designed to be user-friendly, often requiring only a few clicks to get started.

Github Copilot

If you’ve been working in software development for any length of time, you have likely heard of this one. GitHub Copilot is powered by OpenAI's Codex, a descendant of the GPT-3 family, but fine-tuned specifically for code completion. It works by analyzing the vast repositories of code available on GitHub to learn coding patterns, styles, and best practices, such as comments, function names, and the code itself, to generate syntactically correct and logically fitting code suggestions.

Like Pieces, GitHub Copilot prides itself on easing the dreaded developer burnout that comes with spending hours on mundane code-monkey tasks. According to GitHub, developers who use Copilot find that it helps them code faster and more efficiently. For instance, in a survey conducted by GitHub, it was found that developers who use Copilot can write code up to 55% faster, reducing the time spent searching for code snippets and solutions online.

Learn what the best free and paid GitHub Copilot alternatives are in our latest post.

Microsoft Copilot

Given how much Microsoft has reshaped technology over the past few decades, it’s honestly surprising that we didn’t get a copilot AI assistant from them sooner. But now that it’s finally here, it does not disappoint.

Microsoft Copilot is part of a broader vision to embed AI into the fabric of software development and beyond. While GitHub Copilot focuses specifically on code suggestions, Microsoft's Copilot aims to be a comprehensive assistant across various Microsoft products and services, including development tools.

CodeWP

This one is a little different from the others in that it is targeted at a niche developer audience: WordPress developers. There are plenty of broad generative AI copilots out there, so having one tailored to a specific tech stack can come in handy. Since WordPress powers 43.1% of all websites, this specialized AI assistant is still useful to a broad audience.

CodeWP can help you with all sorts of WordPress tasks such as generating PHP, JavaScript, and CSS code based on natural language descriptions. For WordPress developers, this means less time googling code snippets and more time focusing on creating custom, high-quality themes and plugins.

SQL.ai

Database programming languages are a completely different beast compared to things like JavaScript, Ruby, or Python. But the fact is that almost every developer needs to work with a database at some point, even if it isn’t fun. Having an AI-powered copilot to help you out can be a massive timesaver.

SQLAI.ai focuses entirely on generating SQL queries for you. Unless you’re a SQL developer with a passion for the language who enjoys every second of typing out the perfect query, you’ll love this one.

There are a few capabilities that this AI code copilot helps out with. The first is that you can type out what you want your SQL queries to do using everyday language. Compared to looking up syntax and making sure you’re doing everything right according to the documentation, this is much faster. You can also optimize or fix your SQL using AI. Paste your query into the box and watch the AI copilot work its magic in a few seconds.

You won’t find a vast extension marketplace like some of the other AI copilots, but the pricing and ease of use can’t be argued with. You can pick plans from $4 per month, or even less if you pay by the year. Another drawback is that this is primarily an AI copilot for the web. So, you won’t be able to download it or use it within your IDE.

How to Choose the Right AI Copilot for Your Needs

When it comes to choosing an AI copilot, the decision boils down to understanding your needs, the unique offerings of each AI copilot, and how they align with your development goals. Here’s how you should approach this selection process.

• Consider your industry focus and project requirements. If you need something for a specific use case such as WordPress or SQL, then you could consider CodeWP or SQL.ai. On the other hand, if you need a more versatile solution, consider something like Pieces for Developers or GitHub Copilot.

• Evaluate the ease of integration and compatibility. If you want to stick to your favorite IDE or software interface, make sure you pick an AI copilot for the web, editor, and other tools, not just one for a specific app.

• Project complexity and tech stack. If your project is simple and limited to one app, such as WordPress, then you might be fine with a more limited AI copilot like CodeWP. Conversely, if your project incorporates multiple programming languages and apps, you might want to consider something with more diverse capabilities.

• Core functionality. Many AI assistants have very basic functionality, like SQL.ai, and they can only do one or two things at a time. Others, such as Microsoft Copilot, can handle a wide range of tasks across your entire operating system.

• Developer support. You’ll want to make sure you pick an AI coding assistant that has extensive documentation and a library of tutorials to help you get started.

• Cost-effectiveness. Many generative AI copilots are free, such as Pieces. But other AI copilots charge a monthly subscription, like GitHub Copilot. Think about your budget when deciding which one is best for you.

Implementing AI Copilots in Your Workflow

Integrating an AI assistant copilot into your daily tasks can significantly boost your team's productivity and streamline operations. Here is a quick step-by-step for integrating an AI-powered copilot into your workflow:

1. Identify opportunities. Look at your daily operations to find areas where an AI copilot can make a difference. This could be anything from data entry and scheduling to more complex problem-solving tasks.

2. Select the right tool. Not all AI copilots are created equal. Choose one that fits well with your team's needs and the specific tasks you want to automate.

3. Start small. Begin with a pilot program. Implement the AI copilot in a small, controlled environment to gauge its effectiveness and identify any adjustments needed.

4. Educate your team. Make sure your team knows how to work with the AI. This might include training sessions or workshops to get everyone up to speed.

5. Evaluate and scale. Continuously assess the AI copilot’s impact on your workflow. If it proves beneficial, consider expanding its role within your organization.

What about Enterprise AI Copilots?

It seems every company out there is adding AI to their toolbox in some way or another. And this is all for a very good reason. AI is a game-changer when it comes to addressing the rapid pace of technological change and the growing complexity of software development.

Enterprises face challenges such as high developer turnover and the need for faster onboarding and AI upskilling of new team members. AI copilots can help with these challenges by offering real-time assistance, automating routine tasks, and enabling more efficient knowledge transfer and collaboration among team members.

As developers, we’re often told to consult the docs when we have a problem. But how often is documentation far more long and convoluted than it needs to be? It isn’t the best part of anyone’s day when we have to sift through pages of unfamiliar documents just to find the solution to a niche issue or bug. A good copilot AI tool can be a huge help here and reduce the cognitive load on developers, helping them to focus on more creative and strategic aspects of their work.

Becoming an AI-powered enterprise isn’t all that difficult either, and the leap is often more than worth it.

How to Build an AI Copilot

Building your own free AI copilot is a great way to both learn about the underlying tech and customize it to your heart’s content. We’ll give you a quick rundown of how to do this with Pieces. Start with understanding the Pieces OS Client, which is essentially the heart of your custom copilot. It's a database that comes with the advantage of built-in LLMs and the ability to set custom context for your conversations.

When you're setting up, you'll want to choose an SDK that fits your language preference. Pieces has made it rather convenient by offering a variety of SDKs including Typescript, Python, Kotlin, and Dart. This means you can work in a language you're comfortable with and still leverage the powerful features of Pieces OS.

Once you've downloaded the desired SDK, such as the Typescript SDK, and installed Pieces OS on your machine, you're ready to start crafting your copilot. The beauty of this setup is that you can start making requests to your copilot with minimal setup, which is great for getting up and running quickly.

Depending on your requirements, you can manage and use various cloud-based and local LLMs. Download them locally with the help of modelsProgressController.tsx for offline use, or connect to cloud-based models if you prefer.

If you want, you can enhance your copilot’s responses by adding context to your queries. Unlike many environments where you might have to retrain models with your data to get relevant answers, Pieces allows you to attach context to your questions, so you can get more accurate and tailored responses without the wait.

You can check out our in-depth guide on building your own copilot AI here.

The Future of AI Copilots

Industry leaders at Andreessen Horowitz say the era we're entering could be akin to a new Industrial Revolution for cognitive tasks, suggesting a significant boost in productivity for knowledge workers. AI copilots serve as early examples of how AI can make knowledge work more efficient and enjoyable, pointing towards a future where the collaboration between humans and AI leads to unprecedented levels of productivity and innovation.

Pieces is at the forefront of this revolution, directly enhancing the efficiency and productivity of developers by integrating AI-powered solutions into their workflows. The ability to create personalized suggestions and automate parts of the coding process, grounded in the unique context of each developer's work, embodies the promise of AI copilots in transforming cognitive tasks.

All of this technology was a pipedream just a few years ago. But now it is a reality. If you want to see how, just download Pieces and try it out for yourself.