What is AI pair programming?
Written with insights from Atharva, AI Lead at Algorizz. AI pair programming is a way of writing software in which a developer and an AI coding assistant work on the same task together, taking turns to build, test, debug and improve the code. The idea borrows from classic pair programming[1], where two engineers share one workstation: one writes the code while the other reviews each line and thinks about what could go wrong. In the AI version, the second seat is taken by a model.
At Algorizz Technologies, this has become the default way engineering teams use AI. The principle is simple. Developers no longer need to write every line themselves, but they cannot hand an entire application to an AI and trust what comes back. Atharva describes the model as an engineering partner, not an autopilot.
“AI builds, you test. Or you build, AI tests. Either way, the engineer stays accountable.
Why it matters now
AI coding assistants are now part of everyday development. In the 2024 Stack Overflow Developer Survey[4], a large majority of respondents said they were using or planning to use AI tools in their development process. Early controlled research is encouraging: in one GitHub Copilot experiment[3], developers with AI help completed a programming task about 55% faster than those without it.
Speed is not the whole picture. Google’s DORA research[5] found that while AI adoption improved how developers felt about their productivity and code quality, it was also associated with lower software delivery stability. Algorizz reads that as a warning: more code, produced faster, is only an advantage if it is verified. That is the gap AI pair programming is designed to close.
Mode one: AI builds, you test
In the first mode, the engineer acts as product owner and reviewer. They explain the feature, define the requirements and acceptance criteria, and let the AI write the implementation. The engineer’s job then shifts to verification.
That review is where the real work happens. Algorizz engineers read the generated code line by line rather than skimming it, test different scenarios, look for bugs and edge cases[7], and give the AI specific feedback. The cycle repeats until the feature behaves correctly. This mode suits well-understood features, boilerplate, integrations and first drafts, where the engineer knows exactly what “correct” looks like.
Mode two: you build, AI tests
In the second mode, the roles reverse. The engineer writes the feature and asks the AI to act as a testing partner, much like the reviewer in traditional pairing. A typical instruction might read: “I have built a login API. Write test cases for invalid credentials, missing inputs, authentication failures and edge cases. Don’t modify my implementation yet.”
The final line matters. By telling the AI not to change the code, the engineer keeps control of the design and uses the AI purely to surface what they may have missed. The approach sits close to test-driven development[6], and it works best for core business logic, security-sensitive paths and code where the engineer’s own judgement should shape the implementation.
How to choose between the two modes
Algorizz teams decide by asking where the risk lies. If the requirement is clear and the cost of a mistake is low, letting AI build and having the engineer test is usually faster. If the logic is novel, the domain is regulated or the code handles authentication, payments or personal data, the engineer builds and the AI tests. Many features use both: AI drafts a scaffold, the engineer rewrites the critical parts, and AI then generates the test suite.
A step-by-step workflow to get started
Algorizz recommends starting small rather than rolling AI out across a whole codebase at once. The workflow it uses with teams has five steps.
First, pick one small feature from an existing project. Second, define clearly what the feature should do, including inputs, outputs and failure cases. Third, use an AI coding assistant to implement it or to test it, depending on the mode. Fourth, review the code instead of accepting it blindly, and run the tests. Fifth, share the actual error messages and failing test output with the AI, rather than a vague description, and repeat until the feature meets its requirements.
Tools such as Cursor[10], GitHub Copilot[11] and Claude Code[12] all support this loop, whether inside the editor or from the terminal.
Guardrails Algorizz puts in place
Pairing with AI does not remove the need for engineering discipline. It raises it. Algorizz teams treat AI-generated code like a pull request from a new colleague: it goes through normal code review[8], it must pass the test suite, and it never goes to production on the strength of a single prompt.
Security gets particular attention. AI assistants can suggest outdated libraries, insecure patterns or code that leaks sensitive data, and applications that call language models bring their own risks, catalogued in the OWASP Top 10 for LLM applications[9]. Engineers are expected to check dependencies, keep secrets out of prompts and run security checks before merging.
The wrong measure of productivity
According to Atharva, AI Lead at Algorizz, the most common mistake developers make is judging AI productivity by how much code it generates. Writing code is only one part of software engineering. Understanding requirements, making architectural decisions, testing edge cases, debugging failures and ensuring reliability[13] matter just as much, and those remain the engineer’s responsibility.
Algorizz therefore looks at outcomes rather than output: how quickly a verified feature reaches production, how many defects escape to users, and how much time engineers spend on design and problem-solving instead of repetitive code.
“The future isn’t about AI replacing developers who can code. It’s about developers who can use AI to build, verify and ship better software.
Frequently asked questions
Is AI pair programming the same as letting AI write the code? No. In AI pair programming the developer stays in the loop at every step, either reviewing what the AI builds or directing the AI to test what the developer has built. Accountability for the result stays with the engineer.
Which tools support AI pair programming? Most modern AI coding assistants do, including Cursor, GitHub Copilot and Claude Code. The workflow matters more than the tool: clear requirements, careful review, real test output and repeated iteration.
Does AI pair programming replace code review? No. AI-generated code should go through the same review, testing and security checks as any other code. At Algorizz it is reviewed as if it came from a new team member.
Where should a team start? With one small, well-defined feature in an existing project, using either mode, and expanding only once the team trusts its review and testing process.
The real skill
For Algorizz, the lesson is that prompting AI to build an application is not the skill that matters. Working with AI as an engineering partner is: knowing when to let it build, when to make it test, and how to verify everything it produces before it reaches a customer.
