Context-Augmented Code Generation: How Product Context Improves AI Coding Agent Decision Compliance by 49%
AI coding agents powered by large language models can read codebases and produce functional code, but they routinely violate team-specific product decisions that are invisible in the source code alone. We introduce a controlled benchmark measuring decision compliance, the rate at which an AI coding agent follows established product, design, and engineering decisions, across 8 realistic software engineering tasks containing 41 weighted decision points. We compare a baseline configuration (Claude Code with codebase access only) against an augmented configuration that adds Brief, a product-context retrieval system providing spec generation, mid-build consultation, and retrieval of recorded d...
| Authors | Drew Dillon, Kasyap Varanasi |
|---|---|
| Primary category | cs.SE |
| Published | 27 April 2026 |
| Last updated at source | 27 April 2026 |
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