Context-Augmented Code Generation: How Product Context Improves AI Coding Agent Decision Compliance by 49%

Drew Dillon; Kasyap Varanasi

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arXiv · 2605.08112

Context-Augmented Code Generation: How Product Context Improves AI Coding Agent Decision Compliance by 49%

Drew Dillon·Kasyap Varanasi

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Abstract

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 decisions, persona pain points, customer signals, and competitive intelligence. On identical prompts and the same repository, the augmented configuration achieves 95% decision compliance versus 46% for the baseline, a 49 percentage point improvement. Per-decision analysis reveals that the baseline achieves 100% compliance on decisions visible in the codebase and 0-33% on decisions requiring product context, suggesting that product-context retrieval is a key driver of the improvement. We release the benchmark repository, all 16 pull requests, and scoring harness for independent reproduction.

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BibTeX RIS

Drew Dillon, Kasyap Varanasi. 2026-04-27. Context-Augmented Code Generation: How Product Context Improves AI Coding Agent Decision Compliance by 49%. https://arxiv.org/abs/2605.08112

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