OCI AI: Generative AI Professional

Course Description

OCI AI: Generative AI Professional builds practical capability across OCI Generative AI architecture, foundation models, service access, compartments, Prompt design, chat patterns, parameters. Rather than treating the application as a set of isolated screens, the course follows the decisions, dependencies, roles, and evidence needed to make the process dependable in day-to-day operations.

The learning journey then moves into Model selection, customization choices, fine-tuning data, evaluation sets, Responsible AI, content safety, data privacy. Activities use generative AI architecture map and authenticated endpoint test, prompt experiment log and grounded-answer evidence, model comparison scorecard and integrated prototype trace, governed release checklist and generative AI monitoring dashboard to compare expected behaviour with actual output, isolate defects early, and document corrective decisions without losing traceability.

By completion, participants can contribute to a secure OCI generative AI solution that selects suitable models, grounds responses, evaluates quality, and operates under clear governance. The final improvement plan identifies practical follow-up actions for monitoring, ownership, exception handling, support readiness, and controlled change.

What You Will Learn

• Explain how OCI Generative AI architecture, foundation models, and service access establish the operating foundation and shape later configuration decisions.
• Configure compartments, policies, dedicated AI clusters, and inference endpoints, then confirm readiness through generative AI architecture map and authenticated endpoint test rather than relying on screen messages alone.
• Execute a controlled workflow covering Prompt design, chat patterns, and parameters, preserving input traceability, approvals, and the resulting changes.
• Read prompt experiment log and grounded-answer evidence to isolate retrieval returning plausible but irrelevant context for a user question, separate symptoms from causes, and select a correction that can be safely retested.
• Integrate Model selection, customization choices, and fine-tuning data with ownership, access, and downstream process expectations.
• Challenge evaluation sets, endpoint deployment, SDK usage, and application integration using normal and boundary cases, then defend the outcome with model comparison scorecard and integrated prototype trace.
• Investigate a production response-quality decline remaining invisible after a model or prompt change by reviewing Responsible AI, content safety, and data privacy before applying a reversible fix.
• Prepare governed release checklist and generative AI monitoring dashboard that demonstrates observability, cost controls, quality monitoring, incident handling, and exam scenario review and supports handover, monitoring, and the next improvement decision.

SkillDynamo Training Approach

Delivery combines focused demonstrations, guided build activities, scenario comparison, evidence review, fault isolation, peer discussion, and a final improvement plan. Each activity requires learners to record assumptions, decisions, expected results, observed behaviour, and the owner of any unresolved action.