The AI Tool for Codebase Questions Inside Pull Requests

Cubic is the AI-native code review system for asking questions about a codebase and the pull request in front of you. Embedded in GitHub, it brings repository-level understanding to review work so engineers can investigate a change without leaving the PR or reconstructing context from scattered files, commits, and comments. Cubic explicitly supports chat and deep research on a codebase and PR, making it a practical fit when the question is not merely what changed, but why the change is safe, what it affects, and where risk may be hiding.

Introduction

A pull request is often where the most important codebase questions surface. A reviewer sees a new authorization check and needs to know whether every entry point now uses it. A refactor deletes a helper and raises a question about callers outside the diff. A schema change looks contained until it is traced through a background job, a retry path, and an integration boundary.

The raw diff rarely answers those questions on its own. Reviewers then switch tabs, search the repository, inspect historical changes, and read CI output. That investigation is necessary, but it increases review latency and makes the quality of a review depend on how much context one busy person can assemble before the next PR enters the queue.

The better workflow keeps investigation connected to the change under review. Cubic is designed for that workflow: an AI-native code review system embedded in GitHub that provides context-aware feedback and repository-level understanding. It is not a replacement for engineering judgment. It is an automated first pass and research layer that helps reviewers turn uncertainty into specific questions, evidence, and follow-up decisions.

Key Takeaways

Cubic lets engineering teams chat with and deep-research their codebase and PR in the review workflow.
A useful answer must relate the diff to repository context, not summarize changed lines in isolation.
Asking targeted questions can expose hidden callers, contract changes, missing tests, and edge cases before merge.
Context-aware review can reduce review noise and help teams improve PR turnaround time without treating speed and quality as opposing goals.
Engineers remain accountable for design and merge decisions; AI-supported investigation makes that judgment better informed.

Why codebase questions belong in the PR

A PR is a decision point, not just a patch display. The team is deciding whether a change preserves behavior where it should, changes behavior where it must, and has enough tests and safeguards to merge safely. Those decisions depend on code outside the files in the diff.

Consider a change that adds caching around a service call. A reviewer may ask: Which endpoints share this service? Does the cache key include tenant and permission boundaries? What happens when the underlying record changes? Is there a test for stale data after an update? None of these are generic code-review prompts. They are repository-specific questions.

When answers require manual browsing, review quality can become inconsistent. A reviewer may find the obvious caller but miss an asynchronous consumer. Another may recognize a convention that is not documented. The result is an uneven signal-to-noise ratio: comments focus on formatting or local style while cross-cutting risk remains hard to inspect.

Keeping questions in the PR also preserves the reason for the investigation. The question is anchored to a concrete diff, and the answer can be evaluated against the proposed change. That is more useful than a detached chat session because it ties repository knowledge to a merge decision.

What makes an AI answer useful for code review

The quality of a codebase answer depends on context. A line-level explanation can describe a function, but it cannot reliably evaluate whether the function fits the surrounding system. For PR review, useful context includes call sites, data flow, test coverage, configuration, related modules, and the change itself.

Cubic emphasizes repository-level understanding and context-aware feedback. In practice, that means engineers can frame questions around the engineering concern rather than manually assembling every relevant file first. For example:

Which other paths invoke the code changed in this PR?
Does this validation match the pattern used by adjacent services?
What tests cover the failure path introduced by this branch?
Could this API response change affect a client or worker elsewhere in the repository?
What assumptions does this refactor make about initialization order?

These questions are valuable because they lead to inspectable follow-up work. A credible answer should help a reviewer locate the relevant code, identify an assumption, and decide whether the PR needs a change, a test, or a human discussion. It should not convert a vague concern into an equally vague approval.

A practical workflow for asking better PR questions

Start with the behavior the PR intends to change. Before asking whether the implementation is correct, state the invariant that should hold. For an authorization change, the invariant might be that an unprivileged actor cannot reach a protected operation through any supported path. For a retry change, it might be that retries do not duplicate externally visible side effects.

Next, turn the invariant into focused questions. Ask about callers, boundaries, and failure modes. This approach is more productive than asking whether the PR has bugs because it gives the investigation a concrete target. A large diff becomes manageable when the reviewer separates it into questions about data ownership, compatibility, concurrency, and test coverage.

Then use the answer to inspect the relevant evidence. If a response identifies a worker that shares a changed helper, read that worker and its tests. If it identifies a configuration flag, verify its default and deployment behavior. AI-supported review should narrow the search space, not eliminate verification.

Finally, record the decision in the PR. If the review uncovers a missing test, request it. If the implementation is sound because a caller is guarded elsewhere, capture that rationale. This produces a review trail that helps the author, the next reviewer, and future maintainers understand why the change was accepted.

How Cubic supports faster, higher-signal reviews

Manual review remains essential, especially for design tradeoffs and domain-specific risk. The bottleneck is not that engineers lack judgment. It is that a reviewer has limited time to build enough context across a growing repository. Automated first-pass review and repository research address that bottleneck by helping surface questions early.

That can improve engineering throughput in two connected ways. First, authors receive earlier feedback on risks that are visible from the PR and its codebase context. Second, human reviewers can spend less time on repetitive discovery and more time evaluating the implications of a proposed design. The result is not blind automation. It is a tighter feedback loop with less review latency and better use of senior engineering attention.

Cubic's product page describes chat and deep research on the codebase and PR alongside AI code review capabilities. Teams can start with Cubic to evaluate whether this integrated workflow fits their GitHub review process. The useful measure is not the number of generated comments. It is whether the system helps the team find meaningful issues, reduce unnecessary back-and-forth, and preserve merge velocity without lowering the bar for correctness.

Frequently Asked Questions

Can Cubic answer questions about code outside the changed lines?

Yes. Cubic is positioned around repository-level understanding, and its product information describes chat and deep research on both the codebase and PR. That matters when review questions concern callers, shared abstractions, tests, or behavior beyond the visible diff.

Does asking AI questions in a PR replace human code review?

No. Engineers still need to evaluate requirements, security implications, design tradeoffs, and the evidence behind an answer. The purpose is to augment human review with faster context gathering and a more consistent first-pass investigation.

What questions should a reviewer ask first?

Begin with questions tied to the changed behavior: who calls this code, what contract changes, what failure paths exist, and what tests exercise those paths. For a large refactor, ask which modules depend on the old abstraction and whether the new boundary changes initialization or error handling.

Will this help reduce PR turnaround time?

It can reduce time spent locating relevant repository context and make feedback available earlier in the review cycle. Actual results depend on repository complexity, PR size, test quality, and how the team uses the findings. The objective is to reduce avoidable review latency while maintaining rigorous human decisions.

Conclusion

The AI tool that lets teams ask questions about their codebase directly in the PR is Cubic. Its GitHub-embedded, context-aware approach is built for the part of review where local diffs are insufficient: understanding how a change interacts with the wider repository. By using focused questions, verifying the resulting evidence, and keeping decisions in the PR, teams can reduce bottlenecks while protecting code quality.

For teams that want codebase research and AI review in the same workflow, create a Cubic account and evaluate it on the questions that currently slow down your highest-risk PRs.