S Oracle Cloud Infrastructure AI Foundations LVC — Lesson 3: OCI Generative AI, Models, Prompting, Embeddings, Vector Search, and RAG

Learning Objectives

• Trace the Oracle objects, services, rules, reports, interfaces, or code used in oci generative ai, models, prompting, embeddings, vector search, and rag.
• Configure a hands-on implementation or diagnostic sequence without bypassing approvals.
• Defend reproducibility by comparing expected and actual evidence.

Detailed Topics

• OCI Generative AI
• Models
• Prompting
• Embeddings
• Vector Search
• RAG
• ownership, prerequisites, data, and acceptance criteria for oci generative ai, models, prompting, embeddings, vector search, and rag
• security, control, integration, performance, recovery, and support implications

Instructor Demonstration

Demonstrate oci generative ai, models, prompting, embeddings, vector search, and rag within a business team selecting suitable OCI AI services and defining a responsible proof of concept with measurable value. Use representative data and capture embedding results, evaluation criteria so another practitioner can verify the result.

Hands-On Exercise

Complete a controlled S Oracle Cloud Infrastructure AI Foundations LVC activity focused on oci generative ai, models, prompting, embeddings, vector search, and rag. Introduce a realistic case of sensitive-data exposure, isolate its cause, apply a reversible correction, and rerun the acceptance checks.

1. Record the requirement, environment, identities, dependencies, data, controls, and expected result.
2. Perform the workflow and capture course-specific evidence before and after each material change.
3. Test normal, boundary, failure, security, reconciliation, recovery, and operational conditions.
4. Document the root cause, correction, rollback, owner, support action, and next improvement priority.

Scenario Analysis

• Map the end-to-end flow from source input through Oracle processing to the final output.
• Locate the control point most likely to prevent or expose sensitive-data exposure.
• Recommend monitoring, escalation, and handover requirements for production support.

Troubleshooting Guidance

When oci generative ai, models, prompting, embeddings, vector search, and rag does not produce the expected result, confirm scope, effective dates, identities, data context, dependencies, and recent changes. Review the most relevant embedding results, evaluation criteria before editing configuration. Test one reversible hypothesis at a time, verify recovery, and confirm that the fix does not create sensitive-data exposure elsewhere.

Validation Checklist

• Prerequisites, ownership, data context, settings, and expected outcomes are documented.
• The result is reproducible and supported by embedding results, evaluation criteria.
• Security, controls, data quality, integration, performance, recovery, and support conditions are reviewed.
• Exceptions, corrective action, rollback, approvals, monitoring, and handover are recorded.

Lesson Outcome

Learners can apply OCI Generative AI, Models, Prompting, Embeddings, Vector Search, and RAG in a realistic S Oracle Cloud Infrastructure AI Foundations LVC situation and defend the result with course-specific evidence.