The retrieval step: finding relevant content
Retrieval means searching an existing knowledge base — typically using vector-embedding-based semantic search, which matches on meaning rather than exact keyword overlap — to find the content most relevant to a given query or live conversation. This step surfaces existing, previously authored content; it does not itself create anything new. A retrieval system can be evaluated on its own, independent of whether a generation step follows it, using the precision/recall framework described in the companion piece on measuring retrieval accuracy.
The generation step: producing new text grounded in what was retrieved
Generation means a language model takes the retrieved content as context and produces a new, synthesized response — not just displaying the retrieved article, but writing a response informed by it, potentially combining, summarizing, or rephrasing multiple retrieved sources into one answer. This is the step that distinguishes full RAG from retrieval alone: RAG specifically describes using retrieval to ground and improve the factual basis of a generation step, reducing (though not eliminating) the hallucination risk a language model would otherwise carry if it generated a response from its own training alone, without grounding in verified source content.
Why "retrieval alone, feeding a human" is a distinct and meaningfully different pattern
A system that retrieves relevant content and surfaces it to a human agent — who then reads it and decides what to say or do — is doing retrieval, but is not doing RAG, because there is no generation step: no language model is producing new synthesized text as the system’s output. The retrieved content grounds a human’s decision directly, rather than grounding an LLM’s generated response. This is a materially different architecture with a materially different risk profile: the retrieval step can still surface an irrelevant or outdated result (a retrieval-accuracy risk), but there is no generation step that could hallucinate on top of what was retrieved, because nothing is being generated.
Getting Voz360’s Context Retrieval positioned accurately: retrieval-based grounding, not (yet) full RAG
Voz360’s Context Retrieval is vector-embedding-based knowledge search that matches live conversation content against a tenant’s approved knowledge base and surfaces the most relevant article to a human agent in real time. That is the retrieval step, doing real, useful work — but Voz360’s currently shipped Answer Engine is rule-based (decision-tree and FAQ matching), not a generative model, and there is no generation step currently pairing with Context Retrieval to synthesize a new response from what it retrieves. Accurately described, Context Retrieval today is retrieval-based grounding that feeds a human agent’s decision, not full RAG. Voz360’s roadmap generative Assist capability, if and when it ships, would be the generation step that could pair with Context Retrieval’s existing retrieval step to become full RAG in the complete technical sense — calling the current, shipped capability "RAG" ahead of that pairing would overstate what the product does today.
Why this precision matters for evaluating any vendor’s "RAG" claim
RAG has become a common marketing term, and it is frequently applied loosely to any system that involves both a knowledge base and an AI component, even when no generation step exists. A useful question for any vendor claiming RAG: does the system generate a new, synthesized response using a language model grounded in retrieved content, or does it retrieve and display existing content (to a human or otherwise) without a generation step? Only the first is technically full RAG; the second is retrieval, a related but distinct and equally legitimate pattern that simply is not RAG in the strict sense.
Can the vendor tell you — in one sentence — which of their AI capabilities are rule-based, which are generative, and which are still roadmap?