From Prompt Engineering to Context Engineering: Lessons from an AI Code Review Product
summary
An engineer from CodeRabbit explains why LLM prompts alone are insufficient for useful AI code review and shows the product’s shift from sending diffs to designing context-management systems. The talk describes three development phases and presents systems that select team conventions, change history, and dependency graphs dynamically so the model gets the right context for each review.
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chapters (20)
- 0:00Introduction
- 1:03Programming agents evolution
- 3:29Code agents commoditized
- 4:11Code review key frontier
- 7:02Code review challenges
- 9:51Three context pillars
- 11:36Prompt vs context engineering
- 13:21Context windows and tokens
- 15:08Essential context for review
- 17:56Code exploration tools
- 18:59MCPs and progressive auth
- 19:42Ephemeral sandboxes
- 21:08Review quality vs noise
- 23:14Live PR review demo
- 25:21Automatic correction with agents
- 26:46Pre-PR review with plugins
- 27:50Benchmarks and results
- 29:13Future: Inference and multi-agent
- 30:15Summary
- 30:36Q&A
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