OCI GenAI Pro case studies
OCI GenAI Pro · Put your knowledge to work in context.
The OCI GenAI Pro credential is presented here as a professional-level certification for practitioners who design, build, and operate generative AI solutions on Oracle Cloud Infrastructure. It suits cloud architects, AI/ML engineers, application developers, and technical consultants who already understand OCI fundamentals and want to demonstrate depth in model selection, inference tuning, retrieval-augmented generation (RAG), and governance of production AI workloads. The OCI GenAI Pro case studies are 5 cases and about 3,357 words.
1Peak-Hour Throttling on a Claims Summarization Workload
A claims platform on OCI uses the Generative AI service's on-demand inference with a Cohere Command R+ chat model to summarize claim packets and extract injury codes. Volume is 9,000 packets per day, averaging 6,200 input tokens and 400 output tokens.
669 words, about 3 min2The Answer Is in the Corpus but Never in the Top Five
A knowledge team has built a RAG assistant over 380,000 product and policy documents in Object Storage, split into fixed 512-token chunks with 25-token overlap, embedded with a multilingual embedding model, and loaded into Oracle Database 23ai AI Vector Search with an HNSW index using cosine distance. Retrieval returns the top 5 chunks with no metadata filter, and a chat model answers with citations.
686 words, about 3 min3An Eleven-Month-Old Key and a Six-Week Deadline
An HR SaaS runs on OCI in an EU region. Dev, test and prod share a single compartment.
689 words, about 3 min4Five Runs, Five Different Answers
An SRE team has built an incident-triage assistant on OCI Generative AI. The prompt includes a 20 KB runbook plus the last 40 log lines and asks the model to return only JSON with the fields severity, service and next action.
654 words, about 3 min5Ninety-One Percent Against a Ninety-Six Percent Bar
A telecom operator receives 60,000 support tickets per quarter, each labelled into one of 42 categories by human agents. A prompt with category definitions and eight few-shot examples on a hosted chat model reaches 91% top-1 accuracy against a 96% bar required to auto-route without human review.
659 words, about 3 min