Practical Context Engineering: Eliminate Bugs with High-Signal AI Code Reviews | NVIDIA GTC

AI-assisted coding has sped up code generation, but human-only code reviews are now the bottleneck. Too many pull requests (PRs), not enough reviewer bandwidth. This session shows how you can build the right context-engineering architecture that helps LLMs deliver high-signal AI code reviews. Catch logical or functional bugs, memory leaks, security vulnerabilities, code refactors, edge cases, and more. You'll see how bringing in the right context from external datasets (such as code graphs, PR history, issue tickets, MCP servers, and linters) yields better-quality reviews of PR diffs. You will leave with a practical playbook to pilot and scale AI code reviews that increase your release velocity with fewer bugs. Speaker: David Loker | VP of AI | CodeRabbit Key Takeaways: Code reviews are a worsening bottleneck to release velocity, and AI coding agents need an independent auditor. Proper context engineering helps LLMs deliver higher signal review comments that catch hidden and critical bugs. Fix 50% more bugs (e.g., logical bugs, refactors, security vulnerabilities) and ship 50% faster with independent AI code reviews. Industry: All Industries Topic: Agentic AI / Generative AI - Code / Software Generation Technical Level: Technical - Intermediate Intended Audience: Developer / Engineer NVIDIA Technology: NVIDIA NIM #nvidiagtc @CodeRabbitAI