Retrieval-augmented generation can manage expectations of AI (www.techradar.com)

AI adoption is accelerating—around 39% of UK organizations already use the technology—but rising implementation has outpaced realistic expectations. The article argues that treating generative models as infallible is harming trust and slowing adoption; instead, organizations should pursue small, fast projects, accept probabilistic errors as learning opportunities, and combine AI with human oversight. Retrieval-augmented generation (RAG) is highlighted as a practical way to manage expectations and improve reliability by grounding model outputs in verifiable, up-to-date documents rather than relying solely on static training patterns. Technically, RAG systems fetch relevant context from external corpora or databases at query time and condition the generator on that material, which reduces hallucinations and produces more context-aware answers. The piece recommends iterative workflows—error analysis, prompt tuning, model swaps, short-term pilots and governance—to continuously improve performance and scale responsibly. The implication for the AI/ML community is clear: build human-centric, data-connected pipelines (RAG + oversight) and favor agile, incremental deployment over long, top-down transformations to increase trust, lower risk, and accelerate practical value delivery.