AI · · WebLab Corp editorial
Learn what retrieval-augmented generation adds to an AI web product, where it helps, and why evaluation still matters.
Give the model relevant context
IBM explains retrieval-augmented generation, or RAG, as connecting a model with external knowledge. Relevant material is retrieved and added to the prompt before an answer is generated. This can support a product assistant that needs information beyond the model's training data.
Useful context is not a correctness guarantee
IBM also notes that RAG does not make a model error-proof. WebLab Corp's implementation perspective is to test the whole user task: retrieval quality, source visibility, unavailable information, and the response when the system should not answer.
Start with a bounded knowledge task
Choose one collection of approved documents and one audience. Identify who may see each source and how updates will be maintained. Compare answers against a small set of representative questions before expanding the feature.
Sources and further reading
Original WebLab Corp editorial, informed by the linked IBM resources. IBM does not sponsor or endorse this article. Implementation recommendations are WebLab Corp's perspective.
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