Applied AI engineering at CodeRabbit.
Software Engineer Intern, Applied AI
Work completed during a summer internship across repository intelligence, model evaluation, deployment, and optimization.
01 / CONTEXT
Cross-repository context for code review.
Changes in one repository can affect packages, services, and downstream applications elsewhere. This work focused on identifying those relationships and making relevant repository context available to code-review systems.
02 / WORK
Areas of responsibility.
The internship included repository-linking systems, evaluation infrastructure, model deployment, and prompt-cost analysis.
The system combines import graphs, SBOM data, architecture signals, and confidence ranking to identify related repositories.
03 / REPOSITORY INTELLIGENCE
Automatic repository linking.
The linking system combines import graphs, SBOM and dependency data, architecture signals, and confidence ranking. It identifies related repositories without requiring a manually maintained map.
CodeRabbit engineering article ↗04 / EVALUATION AND OPTIMIZATION
Model and prompt efficiency.
DeepSeek V4 Flash handled 75% of routed file-summarizer traffic. Removing duplicated file-summary context from a production prompt reduced walkthrough-generation cost by 8–9% while meeting the existing evaluation criteria.
Company and product outcomes are presented at a high level; proprietary implementation details are intentionally omitted.