Oracle GenAI: A Beginner’s Guide to Smarter Workflows
Oracle’s Generative AI is a cloud-native service that allows you to securely run large language models (LLMs) inside OCI—and use them for summarization, drafting, Q&A, and more.
Why OCI GenAI Is a Game Changer
Oracle Cloud’s Generative AI isn’t just about text generation—it’s about embedding intelligence into your existing business systems: Fusion SCM, HCM, ADW, APEX, and more.
You don’t need to train a model or deploy clusters. You just:
Connect your data (securely)
Ask a natural question
Get a structured response in seconds
It’s enterprise GenAI, done right.
Where is GenAI Available?
As of July 2025, Generative AI in OCI is available in:
Ashburn (us-ashburn-1)
Phoenix (us-phoenix-1)
Frankfurt (eu-frankfurt-1)
London (uk-london-1)
Prerequisites
OCI tenancy in a supported region
Compartment enabled for Generative AI
IAM Policies:
allow service generative-ai-family to use generative-ai-endpoint in compartment
Networking via VCN (for production use)
Access to Object Storage / ADW if using RAG
Step-by-Step: Getting Started
1. Enable the GenAI Service
Go to OCI Console → AI & Data Science → Generative AI
Click “Enable Service” for your compartment
Choose Text Generation or Embeddings
2. Deploy a Model
Choose Cohere Command R+ for robust text tasks
Select a VCN + subnet (or create a private endpoint)
Click Create Deployment → Wait until status is Active
3. Create a RAG Pipeline (Recommended)
Rather than pasting raw data into prompts, use RAG (Retrieval-Augmented Generation) to inject business context into every GenAI response.
What Is RAG?
RAG is a method where:
You store documents/tickets/data in ADW or Object Storage
Create embeddings from that data (via GenAI Embeddings model)
When a user asks a question, the system:
Retrieves relevant chunks of your data
Injects those chunks into the prompt
Sends the complete prompt to the model
Result: The LLM answers using your actual business knowledge, not just general training data.
Oracle recently launched OCI Generative AI Agents to simplify this pattern.
Real Use Case: Fusion SCM + APEX + GenAI via RAG
Let’s say you want to build a “Ticket Summary Generator” for your support team:
Architecture Flow:
Tickets are exported from Fusion SCM to Object Storage as JSON files
A background pipeline converts those tickets to embeddings
When a user selects a ticket in APEX, the app:
Queries the most relevant text chunks using vector similarity
Prepares a prompt like:
“Based on this ticket log, summarize the root cause and suggested action for a supply delay.”
[Attached: Retrieved ticket details]
Sends this to OCI GenAI
Result is displayed live in APEX
All processing stays inside Oracle Cloud, with no external model access.
Prompt Engineering for Business Tasks
Role setting:
“You are a supply chain expert summarizing a PO failure…”
Format control:
“Answer in 3 bullets. Use business formal tone.”
Injected context:
(via RAG pipeline)
"PO#1167 failed SLA due to Vendor ABC delay on July 17. SLA breached by 2 days. Resolution logged in internal notes..."
Now that we’ve seen how to structure prompts, let’s look at what you need to build this system end-to-end.
Oracle's RAG blog:
🔗 How Oracle Uses Generative AI Agents for RAG
Built-In Enterprise Controls
SOC2, GDPR, ISO-27001 aligned
IAM, Audit Logging, and Compartment isolation
Data never used for training
Full control over VCN/networking
Cost & Quota Notes:
Usage is measured in input + output tokens per API call, and each deployment has a quota limit you can monitor from the OCI Console under:
Console → Governance & Administration → Limits, Quotas and Usage
Additional Resources
Final Thoughts
OCI GenAI isn’t just for playing with prompts—it’s for injecting intelligence into your workflows.
Using RAG pipelines, you can teach GenAI your enterprise data and automate:
Invoice audits
Procurement dispute summaries
HR memos
CRM ticket escalations
Even field service visit reports
You’re not replacing people—you’re upgrading the tools they use.
One ticket at a time. One prompt at a time. This is how AI scales safely in the enterprise.
Conclusion
What did you find most useful here?
I’d love to hear how you’re applying these ideas in your work — drop a thought or takeaway in the comments.
If this sparked something valuable, feel free to share it with your team or that one friend who’s always exploring new tools.
Coming Next:
“Teach GenAI Your Business: A Deep Dive into RAG Pipelines on Oracle Cloud”