Ankit Jain separates code review into checking correctness and maintaining team alignment. Automated checks can help find bugs and enforce conventions, but mentorship, architectural discussion and knowledge sharing still need a place in the workflow. He argues that increasing AI-generated code makes it more important to redesign review without discarding those human functions.
Ankit Jain explains that specifications alone miss decisions discovered during implementation. Tickets, product requirements and conversations with a coding agent all contain intent, yet prompt history is often discarded when a pull request is created. He proposes turning the human decisions in those sessions into acceptance criteria so reviewers can understand what was requested, clarified and rejected.
Ankit Jain introduces an AI slop registry as a maintained collection of recurring review findings and reusable rules. Combining those rules with acceptance criteria produces a test plan that can guide verification in an application preview. The proposed review material becomes the intended behavior and collected evidence rather than only a code diff.
Ankit Jain describes a hybrid verification approach: deterministic checks where possible, with agents or LLM judgments where necessary. A payment-form example uses browser interactions, screenshots and database snapshots to assess whether the intended behavior occurred. He warns against deriving the entire test plan from the generated code, because that can repeat the implementation's mistaken assumptions.
Ankit Jain argues that reviewers should assess intent, architectural choices and verification results, with test plans written clearly enough for product managers and designers to participate. Building the reusable rule registry takes initial effort, but each captured pattern can reduce repeated review comments while preserving team collaboration.
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