Rethinking Code Review: How RAG Brings Context to AI Feedback
Developers are often tasked with identifying critical issues in unfamiliar code, under tight deadlines, and with limited context. While AI review tools aim to assist, many focus solely on the diff—overlooking related files, architectural decisions, and historical changes. This approach can result in shallow feedback and missed risks.
This webinar delves into how Retrieval-Augmented Generation (RAG) introduces meaningful context to AI-assisted code reviews. By retrieving relevant code, documentation, and PR history, RAG provides context-aware insights that reflect the broader structure and behavior of the code. Gemini 2.5 leverages these insights to deliver more accurate, context-sensitive suggestions that align with the actual workings of the codebase.
Attendees will walk through real examples, comparing the same pull request reviewed with and without RAG. We will demonstrate how context can surface issues like configuration drift, API mismatches, and architectural inconsistencies, while also enhancing review consistency across complex, multi-repo codebases.
What you’ll learn:
- How RAG techniques—such as code-aware chunking, semantic indexing, and retrieval using code embeddings—provide accurate, real-time context for AI models
- Common types of issues (config, logic, dependency) that require multi-file or historical context
- How AI review systems using RAG and Gemini 2.5 can improve trust and consistency at scale
- A live walkthrough of context-driven reviews using practical examples
