Why retrieval accuracy is a different question than generative accuracy
A retrieval system searches an existing, approved knowledge base and returns the article or passage most semantically similar to the live conversation — it does not write new content. This means the content itself carries whatever accuracy it had when a human authored and approved it; the retrieval system’s own accuracy question is narrower: did it find and surface the right existing content, not whether it invented something false. This is the meaningful distinction from the hallucination risk described in the companion piece on generative AI hallucination — retrieval accuracy and generative accuracy are measuring genuinely different failure modes.
What "relevance" actually means as a measurement
Relevance in a retrieval context is typically evaluated by having reviewers judge, for a sample of real queries, whether the top-surfaced result(s) were actually the most useful match available in the knowledge base — common metrics include precision (of the results surfaced, how many were genuinely relevant) and recall (of all the relevant content that existed in the knowledge base, how much of it got surfaced). A retrieval system can fail in either direction: surfacing irrelevant content (a precision failure, wasting an agent’s time) or missing genuinely relevant content that exists but wasn’t matched (a recall failure, leaving an agent without help they should have had).
Why embeddings-based semantic search improves on simple keyword matching, and where it still falls short
Vector-embedding-based search matches on semantic meaning rather than exact keyword overlap, so it can surface a relevant article even when a customer’s phrasing doesn’t share vocabulary with the source content — a meaningful improvement over literal keyword search for natural conversational language. It is not infallible: a knowledge base with genuinely ambiguous or overlapping content can still produce a wrong top match, and semantic similarity is not the same as correctness — a semantically similar but subtly outdated article can still be surfaced confidently. Retrieval accuracy still depends heavily on the quality and currency of the underlying knowledge base being searched.
What ongoing measurement looks like in practice
A defensible retrieval-accuracy program samples real production queries periodically, has a reviewer judge whether the surfaced result was genuinely the best available match, and tracks that judgment over time as the knowledge base and query patterns evolve — not a one-time benchmark run at launch and never revisited. Because relevance judgment is inherently somewhat subjective (reasonable reviewers can disagree on borderline cases), a useful practice is periodic spot-check review by the team that owns the knowledge content, rather than a single automated score treated as definitive.
How this connects to Voz360’s Context Retrieval, without a specific number
Context Retrieval is Voz360’s vector-embedding-based knowledge search: it matches live conversation content against a tenant’s approved knowledge base and surfaces the most relevant article to an agent in real time. Consistent with this piece’s framework, evaluating that capability well means sampling real conversation queries against a specific tenant’s actual knowledge base and judging relevance directly, rather than relying on a generic, vendor-published accuracy percentage — no such percentage is claimed here, deliberately, since retrieval accuracy depends heavily on the specific knowledge base and query patterns of the organization using it, and a number measured on one knowledge base would not transfer meaningfully to another.
Can the vendor tell you — in one sentence — which of their AI capabilities are rule-based, which are generative, and which are still roadmap?