Connecting coding agents to the right documentation with a navigable context layer

A Redis team describes redesigning an agentic documentation retrieval system to provide precise, cited answers from redis.io/docs by moving beyond a single broad document entity. Initial evaluations showed that fluent AI answers could conceal weak evidence retrieval, prompting the creation of a reusable evaluation framework using a 62-question golden dataset, ranked source IDs, and metrics including nDCG, recall, hit rate, latency, and tool calls. The original model treated pages, sections, and commands alike, causing failures such as irrelevant results for exact command queries like BF.RESERVE. The revised design separates documentation into page summaries for discovery, focused chunks for evidence, and structured command records for direct lookups, linking them through stable page IDs so agents can navigate between related records. Compared with the first version, the new model increased overall nDCG@5 from 0.449 to 0.619 and recall@5 from 0.610 to 0.743, although median latency rose from 6.768 to 10.481 seconds and median tool calls doubled from two to four. The authors conclude that retrieval systems should reflect real user intents, use stable identities and explicit relationships, test retrieval quality independently from answer quality, and optimize richer retrieval paths to reduce unnecessary calls and latency.

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