Uploaded July 2026 | Updated September 2026, 2 weeks ago
Learn More: bit.ly/4pCoIqu
Learn how to make AI code review effective and build your own review agent in this short course, "AI Code Review," built in collaboration with Qodo and taught by Nnenna Ndukwe.
The quality of an AI code review depends more on the context the reviewer has than on the model behind it. As AI writes a bigger share of your team's code, that context is what keeps reviews reliable: a pull request can look reasonable while missing a requirement or hiding a security issue, and there's often too much of it to check by hand.
You'll start with practical techniques for getting more out of AI review, like reviewing before you open a pull request, giving the reviewer task and repository context, and triaging findings by risk. Then you'll build a context-aware review system of your own, beginning with a context engine that retrieves the most relevant code and extending it into an ensemble of specialized agents.
In detail, you'll:
- Understand what makes a good code review, and how AI review differs from human review.
- Apply the practices that make AI review more effective: run it before you open a pull request, use a reviewer that didn't write the code, and give it the task and repository context it needs.
- Triage findings by risk, and turn good feedback into reusable standards so each review sharpens the next.
- See why context-aware review catches what a diff-only review misses, then build a context engine with chunking, embeddings, and vector search.
- Combine specialized agents, like a security expert and a codebase-pattern expert, into an ensemble that outperforms a single general reviewer.
By the end, you'll be able to catch the security holes, missed requirements, and pattern violations that slip through today, and build a reviewer that keeps finding them.
Enroll now: bit.ly/4pCoIqu
Learn More: bit.ly/4pCoIqu
Learn how to make AI code review effective and build your own review agent in this short course, "AI Code Review," built in collaboration with Qodo and taught by Nnenna Ndukwe.
The quality of an AI code review depends more on the context the reviewer has than on the model behind it. As AI writes a bigger share of your team's code, that context is what keeps reviews reliable: a pull request can look reasonable while missing a requirement or hiding a security issue, and there's often too much of it to check by hand.
You'll start with practical techniques for getting more out of AI review, like reviewing before you open a pull request, giving the reviewer task and repository context, and triaging findings by risk. Then you'll build a context-aware review system of your own, beginning with a context engine that retrieves the most relevant code and extending it into an ensemble of specialized agents.
In detail, you'll:
- Understand what makes a good code review, and how AI review differs from human review.
- Apply the practices that make AI review more effective: run it before you open a pull request, use a reviewer that didn't write the code, and give it the task and repository context it needs.
- Triage findings by risk, and turn good feedback into reusable standards so each review sharpens the next.
- See why context-aware review catches what a diff-only review misses, then build a context engine with chunking, embeddings, and vector search.
- Combine specialized agents, like a security expert and a codebase-pattern expert, into an ensemble that outperforms a single general reviewer.
By the end, you'll be able to catch the security holes, missed requirements, and pattern violations that slip through today, and build a reviewer that keeps finding them.
Enroll now: bit.ly/4pCoIqu
