Some days, coding feels like flying. You’re in the zone, every piece of code you write just clicks into place, and you realize why you fell in love with it in the first place.
Other days? It’s you, an endless stack of tabs, a stubborn bug, and the creeping feeling that this is going to eat up your entire day.
AI pair programming is for those days (and well, for good ones, too).
It’s your never-tired coding partner that can spot an error in your code, suggest an alternative approach when you’re stuck in a rut, and even write entire test cases from scratch!
In this article, we’ll break down AI pair programming, its benefits, and see how it stacks up against its traditional counterpart. We’ll also explore some popular AI pair programming tools that will make your life easier.
AI Pair Programming: How to Improve Coding Efficiency with AI
What Is AI Pair Programming?
AI pair programming involves collaborating with an AI assistant inside your integrated development environment (IDE) as if it were a second engineer at the keyboard. It’s pretty much like working with a human partner, but with machine learning models handling the other “seat.”
At its core, an AI pair programmer does three things well:
- Understands your goal: Reads your code and project context to grasp the bigger picture of what you’re trying to build or fix
- Proposes changes: Offers intelligent suggestions, which can range from finishing the line of code you’re typing to entire code completion
- Explains and critiques: Breaks down complex blocks of code in plain language, answers your queries, and even explains why its suggestions are better
🚫 What AI pair programming is not: Autopilot. AI won’t decide your architecture or define your security posture. It will only suggest—you’re the one calling the shots on what to include in your code and what to reject.
AI Pair Programming vs. Human Pair Programming
In traditional pair programming, two developers share a single screen—one types the code (aka, the “driver”), and the other reviews it (aka, the “navigator”). Collaborative, sure, but also time-intensive. Plus, finding a partner who matches your pace (and patience) isn’t always easy.
AI pair programming takes the same idea of two brains working on a problem together simultaneously, but swaps the second human (or the navigator) for an AI model.
The upside? The AI navigator is available 24/7 and never gets tired.
And since it’s trained on an enormous codebase, it can offer highly relevant suggestions, provide diverse solutions to a problem, generate code, and identify subtle errors that a human may miss.
| Aspect | Traditional pair programming | AI pair programming |
| Participants | Two human developers working together | One developer working with an AI coding assistant |
| Collaboration style | Continuous verbal discussion, shared decision-making, switching between “driver” and “navigator” roles | Human leads the context and intent; AI suggests code, explanations, or alternatives instantly |
| Knowledge sharing | Strong transfer of domain knowledge, coding practices, and problem-solving approaches between humans | AI provides coding suggestions and explanations but limited in transferring human domain or business knowledge |
| Availability | Requires coordination of time and effort between two developers | Available on-demand, 24/7, without scheduling constraints |
| Learning benefit | Builds teamwork, communication, and mentoring skills | Accelerates individual learning by providing quick feedback and code snippets |
| Speed & productivity | Can be slower due to discussions and consensus-building | Typically faster for writing and debugging code, though review is still required |
| Quality assurance | Peer continuously reviews code for design, style, and correctness | AI reviews patterns and syntax, but may miss context-specific or architectural issues |
| Best use cases | Complex problem-solving, onboarding juniors, improving team cohesion | Rapid prototyping, debugging, boilerplate generation, individual productivity boost |
👀 Did You Know? A controlled experiment at Google, involving 96 full-time engineers, found that AI assistance shortened task completion time by around 21%—though with a wide confidence interval. The study suggests better gains for those who spend more time coding daily
How Does AI Pair Programming Work?
Getting started with an AI pair programmer is not a tough nut to crack. It works much like adding any other plugin or extension. Set it up, configure it, and you’re ready to start coding.
Let’s understand this step-by-step:
- Pick the AI sidekick that fits your seat: Find an AI pair programming tool that works for your project and integrates easily with your setup. Choose a tool that has a reputation for strong data integrity and security
- Install and sign in: Add the extension or plugin to your setup, then create an account to sign in. Make sure you authorize it to access your code/media/files (carefully limit the access if needed)
- Start coding: Get the ball rolling by typing in your queries or requests. Be as clear and specific as possible because the more detail you provide, the better the result will be
- Review suggestions: You can accept and tweak or even reject AI-generated code suggestions. There’s no hard-and-fast rule that you have to adapt your code to what AI says. Remember, you’re in full control
- Ask follow-up questions: Many AI platforms support conversational back-and-forth. So, if you’re unable to understand a suggestion, follow up with a question or nudge it in the right direction
- Test, iterate, and lock it in: Once you’re satisfied with the code, run your tests to check its efficiency. Tweak your prompts if the test fails and refine your code
- Make it part of your habit loop: Start gradually. Use it first for writing documentation for code or unit tests. As you get more comfortable, you’ll naturally find yourself using AI for more complex tasks, such as debugging or even suggesting architectural patterns
Benefits of AI Pair Programming
Below, find five clear benefits of pair programming with AI to enhance your development process:
- Speed up coding without cutting corners: AI pair programmers generate code suggestions in real time, which allows developers to move from idea to execution with less effort
- Improve code quality across the board: Large language models (LLMs) are trained on extensive, high-quality databases. That means your AI tool can recommend solutions that follow best practices. The result? Your code just got cleaner and less prone to errors
- Learn faster while you code: For newer developers or even experienced ones venturing into unfamiliar programming languages, AI acts like a mentor. It explains suggestions, provides contextual suggestions, and answers your doubts
- Keep projects moving even when teammates aren’t around: AI doesn’t log off at 6 PM or need a coffee break. You can keep working productively without waiting for a human partner to review your code
- Focus more on innovation, less on grunt work: It’s easier for developers to stay in a creative flow state for longer when there’s less repetitive work on the plate. By handling routine tasks like writing documentation, AI lightens your load, which boosts developer productivity
🔍 Did You Know: A GitHub survey revealed that developers who used GitHub Copilot finished their tasks 55% faster than those who didn’t. They only took 1 hour and 11 minutes (avg.) to wrap up their task, while the developers who didn’t use GitHub Copilot took 2 hours and 41 minutes!
| Tool | Key features | Best for |
| 🌟 Write code faster, get auto-complete suggestions, and code explanations with Brain 🌟 Organize development tasks into sprints, set due dates, and track statuses 🌟 Write, store, and collaborate on API documentation, setup instructions, reusable code snippets, best coding practices, etc., with Docs 🌟 Discuss implementation details or ask for feedback using Assigned Comments 🌟 Integrate with GitHub, GitLab, and other tools 🌟 Track sprint velocity, bug counts, or deployment status |
Developers and teams who want coding + project management in one place | |
| GitHub Copilot | 🌟 Assign issues directly to Copilot 🌟 Approve/reject code suggestions using Next Edit Suggestions 🌟 Switch between different chatbot models like Claude, Gemini, and ChatGPT to write code 🌟 Use AI agents to automatically pull context from repos + MCP servers 🌟 Work across various IDEs and languages |
Developers already living in GitHub who want fast AI code suggestions and tighter repo integrations |
| Cursor | 🌟 Get automatic multi-line edit suggestions 🌟 Fix coding errors with smart rewrites 🌟 Automatically pull context from your codebase with custom retrieval models 🌟 Drop images into chat to add visual context |
Software developers who want a deeply integrated coding copilot that can debug, refactor, and adapt code at scale |
| Qodo | 🌟 Create chat-guided tests inside your IDE 🌟 Generate, suggest, and edit code using AI in seconds 🌟 Automate task execution with intelligent agentic coding 🌟 Easily switch between AI models depending on the task |
Individual developers and teams who want powerful chat-guided and context-aware code generation |
| Tabnine | 🌟 Get context-aware AI-generated code suggestions 🌟 Deploy securely via VPC, SaaS, or on-premise 🌟 Automate code generation directly from JIRA requirements 🌟 Explain your project in natural language to onboard the AI agent fast |
Companies with strict data/privacy needs that want AI assistance in software development |
| Bolt | 🌟 Develop full-stack apps with AI (aka prompt-to-app) 🌟 Quickly tweak your app’s UI with a powerful visual editor 🌟 Customize under the hood with full access to your app’s source code 🌟 Flexibly add custom code |
Startups, app developers, and freelancers who want to spin up full-stack apps from a prompt in minutes |
| AskCodi | 🌟 Generate, review, and fix your code with AI 🌟 Quickly refactor existing code 🌟 Translate code between different programming languages using the language translator 🌟 Decipher complex code/errors with code and error explainers |
Students, junior devs, and anyone who needs to hone their coding skills with quick explanations |
How complements AI pair programming
When you’re coding, the hard part often isn’t writing the code, but everything that happens around it: planning sprints, tracking bugs, documenting changes, and keeping the team aligned.
, the everything app for work, is an all-in-one solution for your software development team.
Here’s how you can use ’s Software Project Management Platform to centralize your entire engineering lifecycle.
Visualize your workflows with Views
With 15+ Custom Views, including List, Board, Gantt, Calendar, Mind Map, Workload, and more, everyone on the team can switch instantly to the view that fits their task.
Manage sprint tasks visually in a Kanban Board, or forecast timelines with Gantt charts, and balance capacity using the Workload View.
Centralize documentation using Docs
Docs gives development teams a single, connected space to create, organize, and collaborate on documentation.
Developers can create unlimited docs, wikis, and knowledge bases directly inside . With real-time collaboration, you can tag teammates, comment inline, and even turn sections of text into actionable tasks.
Because docs live alongside tasks and projects, they remain connected to ongoing work. Whether you’re documenting API endpoints, capturing sprint retros, or building a knowledge base, everything stays accessible and up to date.
Track progress with Sprints
Managing sprints across spreadsheets or multiple tools quickly causes development teams to lose visibility. Burndown charts don’t reflect reality, blockers surface too late, and backlog priorities get buried.
With Sprints, your dev teams can manage every sprint cycle inside one workspace. Set sprint dates, assign tasks with story points or time estimates, track outcomes with real-time burndown and velocity charts, and automatically roll unfinished items into the next sprint.
It gives developers and PMs a clear, unified view of the entire SDLC.
💡 Pro Tip: Capture sprint planning discussions, standups, code reviews, or architecture debates instantly as structured notes with the AI Notetaker in .
Automate documentation, reviews, and standups using Brain
Brain reshapes how development teams manage the SDLC from planning to release.
Let your team focus on writing and shipping quality code, while Brain handles:
- Automated documentation: Generate specs, architecture notes, and feature briefs in seconds
- Faster code reviews: Create quick test cases and surface edge cases instantly
- Sprint updates and standups: Summarize progress, blockers, and next steps without manual effort
- Comment replies and notes: Draft responses and condense discussions into clear takeaways
Want to know the best practices for using AI to write documentation? This video helps.
Reduce the manual busywork with Automations
Developers shouldn’t have to waste time moving tasks, updating statuses, or managing repetitive sprint chores.
Automations take care of the routine work so your team can focus on building, testing, and shipping software. Some use cases include:
- Streamlined sprint cycles: Automatically move ready tasks from the backlog to the sprint, assign them, and update statuses as progress is made
- Bug tracking made easy: Auto-tag, assign, and prioritize bug tickets as soon as they’re reported
- Smarter code reviews: Trigger notifications to reviewers when a PR-linked task changes status, reducing delays
- Faster handoffs: Auto-assign tasks when work moves to QA, and set due dates to keep testing cycles predictable
- Consistent workflows: Standardize recurring processes—like closing resolved issues, archiving completed sprints, or updating documentation—without manual intervention
🌟 Bonus: While Automations handle predictable workflows, Pre-built Autopilot Agents run in the background and make proactive decisions for your dev team. An agent can not only assign a bug but also adjust sprint priorities when blockers pile up.
Teams can also design Custom Agents tailored to their workflow—like an agent that monitors pull requests, flags high-risk code patterns, or generates release notes automatically.
Together, they transform workflow management from reactive updates into an adaptive, intelligent system that evolves with your process.
Track velocity, bugs, and capacity with Dashboards
Dashboards pulls all your team’s metrics into one live command center. Track sprint velocity, open bugs, PR-linked tasks, deployment schedules, and team workloads—all in real time.
Custom widgets let you add burndown charts, track lead times, or compare team capacity versus actual output. Instead of chasing reports, your dev team sees a single source of truth for the entire SDLC.
Unify your tech stack with Integrations
With Integrations, your code, conversations, and tasks stay connected. Developers can link commits to tasks, trigger automations on PR merges, and receive Slack notifications.
This way, product managers view development progress in , while engineers stay focused in their IDEs—ensuring less context switching, fewer missed updates, and a smoother release process.
📮 Insight: 74% of employees use two or more tools just to find the information they need—while jumping between emails, chat, notes, project management tools, and documentation.
This constant context-switching wastes time and slows down productivity.
As the everything app for work, unifies all your work—email, chat, docs, tasks, and notes—into a single, searchable workspace, so everything is exactly where you need it.
Real-World Use Cases of AI Pair Programming
Companies are already leaning on AI assistants to automate parts of coding and repetitive development-related tasks. Let’s explore five such use cases where AI is making a difference:
Writing unit tests automatically ✍🏼
Believe it or not, but it only takes a few minutes for an AI tool to scan entire functions/modules and suggest well-structured unit tests! You can then review the test case and customize it as needed, rather than writing it from scratch.
📌 Example: Goldman Sachs, a leading global banking investment firm, used Diffblue’s AI to generate Java unit tests at scale. It turned what engineers called “a year’s worth of unit test writing” into an overnight job. They were able to reduce the average time to write each test case from 30 minutes to mere 10 seconds.
Refactoring legacy code 👩💻
Legacy code is the bane of progress. Thankfully, AI assistants can instantly review your code, identify common vulnerable coding patterns, and propose clean refactorings in no time.
📌 Example: StratusNex, a software company, had a monolithic Java codebase riddled with tangled logic, a common fate for legacy applications. Using AI tools like GitHub Copilot helped reduce manual effort by 70% and lowered migration costs by 60%, from €1.2M down to €360K. It also improved developer onboarding by 40% while reducing downtime.
Generating documentation and README files 🗂️
Creating code documentation is a critical but tedious job. An AI assistant can analyze your code, comments, and the entire project structure to auto-generate accurate and well-formatted API documentation/README files.
📌 Example: During the 2024 IBM WatsonxTM Challenge, over 150 internal teams put IBM’s own AI coding assistant to test and reduced documentation generation time by 59%.
In fact, one developer used natural language prompts to generate code that automatically surfaced multiple files lacking documentation. Within minutes, they flagged more than 750 JavaScript files, a task that would have otherwise taken weeks of manual hunting.
Debugging with AI explanations 🪲
Instead of bouncing between tabs to understand what’s wrong with your code, you can simply ask AI to explain the error/bug, why it may have happened, and how you can fix it.
🛠️ In Practice: AWS developers using Amazon Q found that AI could explain errors in plain English, surface root causes, and even auto-generate test cases—saving teams hours of log-file digging.
AI-assisted sprint planning 🤖
Besides hardcore coding, AI tools for development can also help you plan and track the status of your tasks/projects.
📌 Example: For Atrato, a growing startup, managing projects with scattered tools like Notion and Google Drive worked until it didn’t. By consolidating tasks, sprint points, and velocity tracking into ’s user-friendly, AI-powered platform, the team gained visibility into both immediate tasks and the broader roadmap.
The results were hard to ignore: a 30% increase in development speed and a 20% reduction in developer overload.
🌟 Bonus: For developers, some of the most important insights happen outside the IDE—during sprint planning, retros, or ad-hoc whiteboard sessions. The challenge? Translating those conversations into actionable tasks without losing detail.
With Brain MAX’s Talk-to-Text, instead of typing updates or manually writing task comments, engineers just talk, and Brain MAX turns their words into clear documentation—ready to assign, track, and act on.
Limitations and Challenges of Using AI in Pair Programming
Sure, AI supercharges your coding, but it ain’t a superhero. Like any other tool, AI assistants and code editors have blind spots that every developer should be aware of:
Context gaps
An AI assistant is only as good as what it knows. Sharing partial files or giving a vague prompt with missing details will produce a confident but incomplete answer.
✅ Solution: Always feed context upfront (and clearly). Include recent commits, function comments, or architecture notes in your prompts so that the AI generates contextually aware code.
Inherited bias from public code
AI models use public code as their training data, which can include outdated practices and biased code patterns (e.g., choosing libraries that are popular but not secure). As a result, there’s a good chance that your AI code suggestions will echo this bias.
✅ Solution: Balance AI’s assistance throughout the software development lifecycle with human judgment. Teams should regularly review AI-suggested code against current best practices to ensure it aligns with industry standards.
Over-reliance on AI suggestions
It’s tempting to ask the AI for everything (and accept its suggestions blindly), especially when you’re under pressure. However, AI outputs can be riddled with errors, especially when you’re dealing with high-stakes logic or domain-specific tasks.
✅ Solution: Set boundaries when using AI for coding and development. Reserve it for repetitive and time-consuming tasks such as writing unit test cases or generating documentation. Most importantly, always review AI suggestions before accepting them.
Guarding sensitive data
Sending proprietary code to cloud-hosted AI may expose sensitive data and violate your compliance requirements.
✅ Solution: Choose AI tools that have robust data-handling policies in place to ensure zero data retention and safe hosting.
Intellectual property & ethical concerns
Since LLMs are trained on publicly available code, the suggested code snippets may unintentionally duplicate copyrighted code, which could land you in trouble.
✅ Solution: Developers can avoid copyright pitfalls by running AI-generated snippets through code plagiarism checkers. It’s also important for companies to enforce clear review policies in order to avoid unintentional intellectual property violations.
Here’s how a Redditor sums up their AI pair programming experience:
The Future of AI Pair Programming
In the coming years, AI pair programming will be so much more than just “speed.” It will reshape the way developers build, debug, collaborate, and innovate. How? Let’s find out:
A coding partner built just for you
Every developer has a unique work style and preferences. Software development tools of the future will learn these patterns and tailor their suggestions accordingly. For new teammates, such tools can also serve as repository tour guides, explaining why things are built the way they are.
Spotting errors before they happen
Today’s coding assistants catch problems once you’ve written the code. Tomorrow’s AI tools will spot patterns that typically lead to bugs and flag them early. By predicting risky code paths, AI will significantly reduce debugging cycles and give developers greater confidence in their initial drafts.
🧠 Fun Fact: The very first “bug” in 1947 was a real moth stuck in a relay of the Harvard Mark II. Back then, debugging meant literally removing insects. Tomorrow’s AI tools will take that legacy full circle, spotting potential bugs before they creep into your code and preventing hours of troubleshooting.
⚡ Template Archive: Need a structured way to manage bugs? These bug reporting templates make it easy to document errors, prioritize fixes, and streamline QA.
Code that repairs itself
Instead of waiting for developers to review logs after a crash, future AI tools could monitor software in real time and roll out code fixes automatically. For example, an AI platform will be able to detect a memory leak in a production app and patch it on the fly, reducing downtime and saving engineering hours.
Developers as orchestrators, not just coders
In the future, developers won’t spend most of their time writing code. Rather, they’ll guide multiple AI systems and play the navigator role, assigning tasks, validating outputs, and focusing on higher-level architecture.
: Your Dev Team’s New Favorite Sidekick
One thing’s clear: AI isn’t here to replace developers, but empower them.
From writing cleaner code to keeping projects on track, the right AI pair programming tools help developers focus on work that actually moves the needle. is your dev team’s favorite sidekick here.
From automating sprint updates with Brain to streamlining bug tracking through Automations, from visualizing the SDLC in Dashboards to connecting code with Integrations, puts everything your team needs in one place. Plus, with Brain MAX, you can choose your preferred large language model.
When this overhead is taken care of, your team gets to focus on what matters most: driving innovation and enhancing code quality.
Ready to see how AI fits into your team’s software development workflows? Sign up on for free.
Frequently Asked Questions (FAQ)
Enterprise teams can securely take up AI pair programming by choosing a tool that offers SOC 2 compliance, secure cloud deployments, encryption at rest and in transit, and strict data privacy controls.
While AI is excellent at catching common issues such as syntax errors or performance red flags, it lacks the judgment and nuanced decision-making of a skilled developer. Human reviews still play an irreplaceable role in assessing architecture, business logic, and edge cases that AI can’t fully understand.
Most AI tools support various programming languages, including Python, JavaScript, C#, Java, TypeScript, Go, and others. However, this coverage isn’t universal. There are tools that specialize in web development languages, while others support niche or emerging frameworks.
Surprisingly small. Most tools integrate directly into the IDEs developers already use, so the workflow feels similar. You can simply start typing, and AI suggestions or snippets will appear as required.
Everything you need to stay organized and get work done.
