Updated October 2026 · By Prasad C G
AI coding tools write plausible code quickly, but plausible code can still be wrong, insecure, or a poor fit for your system. As code gets cheaper to produce, more of the quality is decided in review.
What the tools are good at
- Boilerplate and repetitive code.
- First drafts of well-understood patterns.
- Scaffolding tests.
- Translating between languages and frameworks.
- Explaining unfamiliar code.
For this kind of work they save real time. The trouble starts when a draft is treated as a finished change.
Where the risks sit
Code that looks right and is wrong
Generated code tends to handle the common path well and miss the edges: empty inputs, time zones, concurrency, partial failures. It compiles, it reads cleanly, and it is wrong in a way only someone who knows the domain will notice.
Security gaps
A model reproduces common patterns, including common mistakes. Missing authorisation checks, unvalidated input, secrets placed in code, and permissive defaults all look normal in a diff.
Dependencies
Suggestions can include packages that are outdated, unmaintained, or do not exist. A person should decide on every new dependency.
Fit with the system
A tool sees the file in front of it and little of the rest of the codebase. It will duplicate a helper that already exists, ignore a convention, or pick an approach that works against the architecture.
Data and licensing
Know what leaves your environment in prompts, and what your organisation's policy says about generated code. The company should settle both once, rather than leave them to each engineer.
What a senior review adds
- It asks whether the change should exist at all.
- It reads for authorisation and data handling as well as style.
- It checks that the tests test the behaviour, not the implementation.
- It holds the architecture of the whole system in mind.
- It puts a named person behind the result.
A process that keeps the speed
- Treat AI output as a draft from a fast junior colleague.
- Keep changes small enough to review properly.
- Require tests with every change, and read the tests.
- Run static analysis, dependency scanning, and secret scanning in the pipeline.
- Give authentication, payments, and data access a second reviewer.
- Write down which tools are allowed to see which code.
The cost question
AI assistance lowers the cost of producing code. A mistake in production costs as much as it always did, so review standards should stay where they were. We look at the numbers in AI-assisted software development in 2026: cost, speed and quality.
How we review code at TechMaven
We are a senior-led studio, and the team that scopes a project is the team that ships it. Every change is reviewed by an experienced engineer, whether or not an AI tool helped write it. For AI features inside a product, see RAG vs fine-tuning and our AI solutions page.